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A fully coupled computational fluid dynamics (CFD) and discrete element method (DEM) model was calibrated using a draw down test (DDT) under submerged conditions. Momentum smoothing and cell clustering were used to model particles that were larger than the cells. The DEM input parameter values were initially set equal to those calibrated for the dry conditions. Under submerged conditions, results showed that the particle-particle coefficient of friction and the drag modifier had an influence on the results. It was found that the drag modifier had to be calibrated, while the particle-particle coefficient of friction, calibrated under dry conditions, could be used for the submerged conditions. A vertical suction pipe validation experiment was conducted. The suction pipe had a constant diameter, but the fluid velocity and the distance the pipe opening was held from the granular bed were varied. The amount of mass (particles) removed as well as the size of the cavity that formed in the material bed were measured and compared to model predictions. The results showed that using the parameter values calibrated in the DDT, too much material was removed (error of 30%). Removing the drag modifier (setting it equal to unity) significantly improved the results (error of 6%). It is concluded that due to the difference in flow mechanism (particle-induced in the DDT versus fluid-induced in the suction pipe), the DDT is not a suitable experiment to calibrate the input parameter values for a suction pipe. It is proposed that the flow mechanism and dynamics of the granular material and the fluid in the calibration experiment should be similar to that of the final application being investigated.

Introduction: Numerical modeling of the intervertebral disc (IVD) is challenging due to its complex and heterogeneous structure, requiring careful selection of constitutive models and material properties. A critical aspect of such modeling is the representation of annulus fibers, which significantly impact IVD biomechanics. This study presents a comparative analysis of different methods for fiber reinforcement in the annulus fibrosus of a finite element (FE) model of the human IVD.Methods: We utilized a reconstructed L4-L5 IVD geometry to compare three fiber modeling approaches: the anisotropic Holzapfel-Gasser-Ogden (HGO) model (HGO fiber model) and two sets of structural rebar elements with linear-elastic (linear rebar model) and hyperelastic (nonlinear rebar model) material definitions, respectively. Prior to calibration, we conducted a sensitivity analysis to identify the most important model parameters to be calibrated and improve the efficiency of the calibration. Calibration was performed using a genetic algorithm and in vitro range of motion (RoM) data from a published study with eight specimens tested under four loading scenarios. For validation, intradiscal pressure (IDP) measurements from the same study were used, along with additional RoM data from a separate publication involving five specimens subjected to four different loading conditions.Results: The sensitivity analysis revealed that most parameters, except for the Poisson ratio of the annulus fibers and C01 from the nucleus, significantly affected the RoM and IDP outcomes. Upon calibration, the HGO fiber model demonstrated the highest accuracy (R2 = 0.95), followed by the linear (R2 = 0.89) and nonlinear rebar models (R2 = 0.87). During the validation phase, the HGO fiber model maintained its high accuracy (RoM R2 = 0.85; IDP R2 = 0.87), while the linear and nonlinear rebar models had lower validation scores (RoM R2 = 0.71 and 0.69; IDP R2 = 0.86 and 0.8, respectively).Discussion: The results of the study demonstrate a successful calibration process that established good agreement with experimental data. Based on our findings, the HGO fiber model appears to be a more suitable option for accurate IVD FE modeling considering its higher fidelity in simulation results and computational efficiency.

The European X-ray Free Electron Laser (European XFEL) is a cutting-edge user facility that generates per second up to 27,000 ultra-short, spatially coherent X-ray pulses within an energy range of 0.26 to more than 20 keV. Specialized instrumentation, including various 2D X-ray detectors capable of handling the unique time structure of the beam, is required. The one-megapixel AGIPD (AGIPD1M) detectors, developed for the European XFEL by the AGIPD Consortium, are the primary detectors used for user experiments at the SPB/SFX and MID instruments. The first AGIPD1M detector was installed at SPB/SFX when the facility began operation in 2017, and the second one was installed at MID in November 2018. The AGIPD detector systems require a dedicated infrastructure, well-defined safety systems, and high-level control procedures to ensure stable and safe operation. As of now, the AGIPD1M detectors installed at the SPB/SFX and MID experimental end stations are fully integrated into the European XFEL environment, including mechanical integration, vacuum, power, control, data acquisition, and data processing systems. Specific high-level procedures allow facilitated detector control, and dedicated interlock systems based on Programmable Logic Controllers ensure detector safety in case of power, vacuum, or cooling failure. The first 6 years of operation have clearly demonstrated that the AGIPD1M detectors provide high-quality scientific results. The collected data, along with additional dedicated studies, have also enabled the identification and quantification of issues related to detector performance, ensuring stable operation. Characterization and calibration of detectors are among the most critical and challenging aspects of operation due to their complex nature. A methodology has been developed to enable detector characterization and data correction, both in near real-time (online) and offline mode. The calibration process optimizes detector performance and ensures the highest quality of experimental results. Overall, the experience gained from integrating and operating the AGIPD detectors at the European XFEL, along with the developed methodology for detector characterization and calibration, provides valuable insights for the development of next-generation detectors for Free Electron Laser X-ray sources.

Surface water flooding represents a significant hazard for many infrastructure systems. For example, residential, commercial, and industrial properties, water and wastewater treatment facilities, private drinking water wells, stormwater systems, or transportation networks are often impacted (i.e., in terms of damage or functionality) by flooding events. For large scale events, knowing where to prioritize recovery resources can be challenging. To help communities throughout North Carolina manage flood disaster responses, near real-time state-wide rapid flood mapping methods are needed. In this study, Height Above Nearest Drainage (HAND) concepts are combined with National Water Model river discharges to enable rapid flood mapping throughout North Carolina. The modeling system is calibrated using USGS stage-discharge relationships and FEMA 100-year flood maps. The calibration process ultimately provides spatially distributed channel roughness values to best match the available datasets. Results show that the flood mapping system, when calibrated, provides reasonable estimates of both river stage (or corresponding water surface elevations) and surface water extents. Comparing HAND to FEMA hazard maps both in Wake County and state-wide shows an agreement of 80.1% and 76.3%, respectively. For the non-agreement locations, flood extents tend to be overestimated as compared to underestimated, which is preferred in the context of identifying potentially impacted infrastructure systems. Future research will focus on developing transfer relationships to estimate channel roughness values for locations that lack the data needed for calibration.

In this paper, the design of a five degrees of freedom (5DoF) underwater manipulator system is presented and discussed. The forward and inverse kinematics problem are solved and shown. Additionally, a system that mimics the human arm movements using a manipulator is proposed. In order to imitate the movement of the human arm, the layout diagram of the IMU sensors and the method of first calibration were presented. The work also presents a device with a simple haptic feedback used to facilitate control of the gripper. The presented approach to the design of a robot control system is dedicated to an underwater robot with the main assumption of reducing the complexity of the system. Taking into account the authors’ knowledge, this approach distinguishes the presented system from solutions known from the literature.

Scientific studies of the Earth’s climate increasingly rely on high-quality satellite observations. The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key sensor onboard a series of satellites [Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar-orbiting Satellite System 1–4 (JPSS-1–JPSS-4)] that generate scientific data from land, ocean, and atmosphere used in these climate models. Providing quality scientific data from space-borne sensors requires the instruments to be well-calibrated. While much of the calibration can be maintained on-orbit, some aspects of the calibration can best be measured prior to launch. One VIIRS parameter that needs to be measured pre-launch is the response versus scan angle (RVS). The RVS measures the relative change in the reflectance of the scanning optics as a function of the angle of incidence. With the RVS, the gain calibration measured on-orbit can be transferred to any scan angle. The JPSS-3 and JPSS-4 instruments have undergone ground testing including the RVS measurements, which is the subject of this work. Results indicate that the measurements are comparable to previous VIIRS builds and are expected to contribute to the generation of high-quality science data once JPSS-3 and JPSS-4 are on-orbit.

The JUNGFRAU detector is a well-established hybrid pixel detector developed at the Paul Scherrer Institut (PSI) designed for free-electron laser (FEL) applications. JUNGFRAU features a charge-integrating dynamic gain switching architecture, with three different gain stages and 75 μm pixel pitch. It is widely used at the European X-ray Free-Electron Laser (EuXFEL), a facility which produces high brilliance X-ray pulses at MHz repetition rate in the form of bursts repeating at 10 Hz. In nominal configuration, the detector utilizes only a single memory cell and supports data acquisition up to 2 kHz. This constrains the operation of the detector to a 10 Hz frame rate when combined with the pulsed train structure of the EuXFEL. When configured in so-called burst mode, the JUNGFRAU detector can acquire a series of images into sixteen memory cells at a maximum rate of around 150 kHz. This acquisition scheme is better suited for the time structure of the X-rays as well as the pump laser pulses at the EuXFEL. To ensure confidence in the use of the burst mode at EuXFEL, a wide range of measurements have been performed to characterize the detector, especially to validate the detector alibration procedures. In particular, by analyzing the detector response to varying photon intensity (so called ‘intensity scan’), special attention was given to the characterization of the transitions between gain stages. The detector was operated in both dynamic gain switching and fixed gain modes. Results of these measurements indicate difficulties in the characterization of the detector dynamic gain switching response while operated in burst mode, while no major issues have been found with fixed gain operation. Based on this outcome, fixed gain operation mode with all the memory cells was used during two experiments at EuXFEL, namely in serial femtosecond protein crystallography and Kossel lines measurements. The positive outcome of these two experiments validates the good results previously obtained, and opens the possibility for a wider usage of the detector in burst operation mode, although compromises are needed on the dynamic range.

Smartphone camera photoplethysmography (cPPG) enables non-invasive pulse oximetry and hemoglobin concentration measurements. However, the aesthetic-driven non-linearity in default image capture and preprocessing pipelines poses challenges for consistency and transferability of cPPG across devices. This work identifies two key parameters—tone mapping and sensor threshold—that significantly impact cPPG measurements. We propose a novel calibration method to linearize camera measurements, thus enhancing consistency and transferability of cPPG across devices. A benchtop calibration system is also presented, leveraging a microcontroller and LED setup to characterize these parameters for each phone model. Our validation studies demonstrate that, with appropriate calibration and camera settings, cPPG applications can achieve 74% higher accuracy than with default settings. Moreover, our calibration method proves effective across different smartphone models (N=4), and calibrations performed on one phone can be applied to other smartphones of the same model (N=6), enhancing consistency and scalability of cPPG applications.

Since the start of the COVID-19 pandemic in 2020, wastewater surveillance programs were established, or upscaled, in many countries around the world and have proven to be a cost-effective way of monitoring infectious disease pathogens. Many of these programs use RT-qPCR, and quantify the viral concentrations in samples based on standard curves, by including preparations of a reference material with known nucleic acid or virus concentrations in the RT-qPCR analyses. In high-throughput monitoring programs it is possible to combine data from multiple previous runs, circumventing the need for duplication and resulting in decreased costs and prolonged periods during which the reference material is obtained from the same batch. However, over time, systematic shifts in standard curves are likely to occur. This would affect the reliability and usefulness of wastewater surveillance as a whole. We aim to find an optimal combination of standard curve data to compensate for run-to-run measurement variance while ensuring enough flexibility to capture systematic longitudinal shifts. Based on more than 4000 observations obtained with the CDC N1 and N2 assays, taken as a part of the National Sewage Surveillance program at the Dutch National Institute for Public Health and the Environment, we show that seasonal and long-term shifts in RT-qPCR efficiency and sensitivity occur. We find that in our setting, using five days of standard-curve data to quantify, results in the least error prone curve or best approximation. This results in differences up to 100% in quantified viral loads when averaged out over a nationwide program of >300 treatment plants. Results show that combining standard curves from a limited set of runs can be a valid approach to quantification without obscuring the trends in the viral load of interest.

BackgroundSystolic blood pressure amplification (SBPA) and pulse pressure amplification (PPA) can independently predict cardiovascular damage and mortality. A wide range of methods are used for the non-invasive estimation of SBPA and PPA. The most accurate non-invasive method for obtaining SBPA and/or PPA remains unknown.AimThis study aims to evaluate the agreement between the SBPA and PPA values that are invasively and non-invasively obtained using different (1) measurement sites (radial, brachial, carotid), (2) measuring techniques (tonometry, oscillometry/plethysmography, ultrasound), (3) pulse waveform analysis approaches, and (4) calibration methods [systo-diastolic vs. approaches using brachial diastolic and mean blood pressure (BP)], with the latter calculated using different equations or measured by oscillometry.MethodsInvasive aortic and brachial pressure (catheterism) and non-invasive aortic and peripheral (brachial, radial) BP were simultaneously obtained from 34 subjects using different methodologies, analysis methods, measuring sites, and calibration methods. SBPA and PPA were quantified. Concordance correlation and the Bland–Altman analysis were performed.Results(1) In general, SBPA and PPA levels obtained with non-invasive approaches were not associated with those recorded invasively. (2) The different non-invasive approaches led to (extremely) dissimilar results. In general, non-invasive measurements underestimated SBPA and PPA; the higher the invasive SBPA (or PPA), the greater the underestimation. (3) None of the calibration schemes, which considered non-invasive brachial BP to estimate SBPA or PPA, were better than the others. (4) SBPA and PPA levels obtained from radial artery waveform analysis (tonometry) (5) and common carotid artery ultrasound recordings and brachial artery waveform analysis, respectively, minimized the mean errors.ConclusionsOverall, the findings showed that (i) SBPA and PPA indices are not “synonymous” and (ii) non-invasive approaches would fail to accurately determine invasive SBPA or PPA levels, regardless of the recording site, analysis, and calibration methods. Non-invasive measurements generally underestimated SBPA and PPA, and the higher the invasive SBPA or PPA, the higher the underestimation. There was not a calibration scheme better than the others. Consequently, our study emphasizes the strong need to be critical of measurement techniques, to have methodological transparency, and to have expert consensus for non-invasive assessment of SBPA and PPA.

Quantum annealing has emerged as a powerful platform for simulating and optimizing classical and quantum Ising models. Quantum annealers, like other quantum and/or analog computing devices, are susceptible to non-idealities including crosstalk, device variation, and environmental noise. Compensating for these effects through calibration refinement or “shimming” can significantly improve performance but often relies on ad-hoc methods that exploit symmetries in both the problem being solved and the quantum annealer itself. In this tutorial, we attempt to demystify these methods. We introduce methods for finding exploitable symmetries in Ising models and discuss how to use these symmetries to suppress unwanted bias. We work through several examples of increasing complexity and provide complete Python code. We include automated methods for two important tasks: finding copies of small subgraphs in the qubit connectivity graph and automatically finding symmetries of an Ising model via generalized graph automorphism. We conclude the tutorial by surveying additional methods, providing practical implementation tips, and discussing limitations and remedies of the calibration procedure. Code is available at: https://github.com/dwavesystems/shimming-tutorial.

Cavity optomechanics is a suitable field to explore quantum effects on macroscopic objects and develop quantum technology applications. A perfect control of the laser noise is required to operate the system in such extreme conditions necessary to reach the quantum regime. In this paper, we consider a Fabry–Perót cavity, driven by two laser fields, with two partially reflective SiN membranes inside it. We describe the effects of amplitude and phase noise on the laser introducing two additional noise terms in the Langevin equations of the system’s dynamics. Experimentally, we add an artificial source of noise on the laser. We calibrate the intensity of the noise, inject it into the system, and check the validity of the theoretical model. This procedure provides an accurate description of the effects of a noisy laser in the optomechanical setup and allows for quantifying the amount of noise.

Maize silage is a key component of feed rations in dairy systems due to its high forage and grain yield, water use efficiency, and energy content. However, maize silage nutritive value can be compromised by in-season changes during crop development due to changes in plant partitioning between grain and other biomass fractions. The partitioning to grain (harvest index, HI) is affected by the interactions between genotype (G) × environment (E) × management (M). Thus, modelling tools could assist in accurately predicting changes during the in-season crop partitioning and composition and, from these, the HI of maize silage. Our objectives were to (i) identify the main drivers of grain yield and HI variability, (ii) calibrate the Agricultural Production Systems Simulator (APSIM) to estimate crop growth, development, and plant partitioning using detailed experimental field data, and (iii) explore the main sources of HI variance in a wide range of G × E × M combinations. Nitrogen (N) rates, sowing date, harvest date, plant density, irrigation rates, and genotype data were used from four field experiments to assess the main drivers of HI variability and to calibrate the maize crop module in APSIM. Then, the model was run for a complete range of G × E × M combinations across 50 years. Experimental data demonstrated that the main drivers of observed HI variability were genotype and water status. The model accurately simulated phenology [leaf number and canopy green cover; Concordance Correlation Coefficient (CCC)=0.79-0.97, and Root Mean Square Percentage Error (RMSPE)=13%] and crop growth (total aboveground biomass, grain + cob, leaf, and stover weight; CCC=0.86-0.94 and RMSPE=23-39%). In addition, for HI, CCC was high (0.78) with an RMSPE of 12%. The long-term scenario analysis exercise showed that genotype and N rate contributed to 44% and 36% of the HI variance. Our study demonstrated that APSIM is a suitable tool to estimate maize HI as one potential proxy of silage quality. The calibrated APSIM model can now be used to compare the inter-annual variability of HI for maize forage crops based on G × E × M interactions. Therefore, the model provides new knowledge to (potentially) improve maize silage nutritive value and aid genotype selection and harvest timing decision-making.

The following article highlights the need for methodological transparency and consensus for an accurate and non-invasive assessment of central aortic blood pressure (aoBP), which would contribute to increasing its validity and value in both clinical and physiological research settings. The recording method and site, the mathematical model used to quantify aoBP, and mainly the method applied to calibrate pulse waveforms are essential when estimating aoBP and should be considered when analyzing and/or comparing data from different works, populations and/or obtained with different approaches. Up to now, many questions remain concerning the incremental predictive ability of aoBP over peripheral blood pressure and the possible role of aoBP-guided therapy in everyday practice. In this article, we focus on “putting it on the table” and discussing the main aspects analyzed in the literature as potential determinants of the lack of consensus on the non-invasive measurement of aoBP.

IntroductionInternal validation techniques alone do not guarantee the value of a model. This study aims to investigate the external validity of the Parental Attitude toward Childhood Vaccination (PACV) scale for assessing parents’ attitude toward seasonal influenza vaccination.MethodsUsing a snowball sampling approach, an anonymous online questionnaire was distributed in two languages (English and Arabic) across seven countries. To assess the internal validity of the model, the machine learning technique of “resampling methods” was used to repeatedly select various samples collected from Egypt and refit the model for each sample. The binary logistic regression model was used to identify the main determinants of parental intention to vaccinate their children against seasonal influenza. We adopted the original model developed and used its predictors to determine parents’ intention to vaccinate their children in Libya, Lebanon, Syria, Iraq, Palestine, and Sudan. The area under the curve (AUC) indicated the model’s ability to distinguish events from non-events. We visually compared the observed and predicted probabilities of parents’ intention to vaccinate their children using a calibration plot.ResultsA total of 430 parents were recruited from Egypt to internally validate the model, and responses from 2095 parents in the other six countries were used to externally validate the model. Multivariate regression analysis showed that the PACV score, child age (adolescence), and Coronavirus disease 2019 (COVID-19) vaccination in children were significantly associated with the intention to receive the vaccination. The AUC of the developed model was 0.845. Most of the predicted points were close to the diagonal line, demonstrating better calibration (the prediction error was 16.82%). The sensitivity and specificity of the externally validated model were 89.64 and 37.89%, respectively (AUC = 0.769).ConclusionThe PACV showed similar calibration and discrimination across the six countries. It is transportable and can be used to assess attitudes towards influenza vaccination among parents in different countries using either the Arabic or English version of the scale.

Introduction: Patient-specific computational fluid dynamics (CFD) models permit analysis of complex intra-aortic hemodynamics in patients with aortic dissection (AD), where vessel morphology and disease severity are highly individualized. The simulated blood flow regime within these models is sensitive to the prescribed boundary conditions (BCs), so accurate BC selection is fundamental to achieve clinically relevant results.Methods: This study presents a novel reduced-order computational framework for the iterative flow-based calibration of 3-Element Windkessel Model (3EWM) parameters to generate patient-specific BCs. These parameters were calibrated using time-resolved flow information derived from retrospective four-dimensional flow magnetic resonance imaging (4D Flow-MRI). For a healthy and dissected case, blood flow was then investigated numerically in a fully coupled zero dimensional-three dimensional (0D-3D) numerical framework, where the vessel geometries were reconstructed from medical images. Calibration of the 3EWM parameters was automated and required ~3.5 min per branch.Results: With prescription of the calibrated BCs, the computed near-wall hemodynamics (time-averaged wall shear stress, oscillatory shear index) and perfusion distribution were consistent with clinical measurements and previous literature, yielding physiologically relevant results. BC calibration was particularly important in the AD case, where the complex flow regime was captured only after BC calibration.Discussion: This calibration methodology can therefore be applied in clinical cases where branch flow rates are known, for example, via 4D Flow-MRI or ultrasound, to generate patient-specific BCs for CFD models. It is then possible to elucidate, on a case-by-case basis, the highly individualized hemodynamics which occur due to geometric variations in aortic pathology high spatiotemporal resolution through CFD.

In this paper we give an overview of factors and limitations impairing deep-sea sensor data, and we show how automatic tests can give sensors self-validation and self-diagnostic capabilities. This work is intended to lay a basis for sophisticated use of smart sensors in long-term autonomous operation in remote deep-sea locations. Deep-sea observation relies on data from sensors operating in remote, harsh environments which may affect sensor output if uncorrected. In addition to the environmental impact, sensors are subject to limitations regarding power, communication, and limitations on recalibration. To obtain long-term measurements of larger deep-sea areas, fixed platform sensors on the ocean floor may be deployed for several years. As for any observation systems, data collected by deep-sea observation equipment are of limited use if the quality or accuracy (closeness of agreement between the measurement and the true value) is not known. If data from a faulty sensor are used directly, this may result in an erroneous understanding of deep water conditions, or important changes or conditions may not be detected. Faulty sensor data may significantly weaken the overall quality of the combined data from several sensors or any derived model. This is particularly an issue for wireless sensor networks covering large areas, where the overall measurement performance of the network is highly dependent on the data quality from individual sensors. Existing quality control manuals and initiatives for best practice typically recommend a selection of (near) real-time automated checks. These are mostly limited to basic and straight forward verification of metadata and data format, and data value or transition checks against pre-defined thresholds. Delayed-mode inspection is often recommended before a final data quality stamp is assigned.

BackgroundPostoperative risk stratification is challenging in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) who undergo artificial liver treatment. This study characterizes patients’ clinical parameters and laboratory biomarkers with different in-hospital outcomes. The purpose was to establish a multi-subgroup combined predictive model and analyze its predictive capability.MethodsWe enrolled HBV-ACLF patients who received plasma exchange (PE)-centered artificial liver support system (ALSS) therapy from May 6, 2017, to April 6, 2022. There were 110 patients who died (the death group) and 110 propensity score-matched patients who achieved satisfactory outcomes (the survivor group). We compared baseline, before ALSS, after ALSS, and change ratios of laboratory biomarkers. Outcome prediction models were established by generalized estimating equations (GEE). The discrimination was assessed using receiver operating characteristic analyses. Calibration plots compared the mean predicted probability and the mean observed outcome.ResultsWe built a multi-subgroup predictive model (at admission; before ALSS; after ALSS; change ratio) to predict in-hospital outcomes of HBV-ACLF patients who received PE-centered ALSS. There were 110 patients with 363 ALSS sessions who survived and 110 who did not, and 363 ALSS sessions were analyzed. The univariate GEE models revealed that several parameters were independent risk factors. Clinical parameters and laboratory biomarkers were entered into the multivariate GEE model. The discriminative power of the multivariate GEE models was excellent, and calibration showed better agreement between the predicted and observed probabilities than the univariate models.ConclusionsThe multi-subgroup combined predictive model generated accurate prognostic information for patients undergoing HBV-ACLF patients who received PE-centered ALSS.

Background: The non-invasive estimation of aortic systolic (aoSBP) and pulse pressure (aoPP) is achieved by a great variety of devices, which differ markedly in the: 1) principles of recording (applied technology), 2) arterial recording site, 3) model and mathematical analysis applied to signals, and/or 4) calibration scheme. The most reliable non-invasive procedure to obtain aoSBP and aoPP is not well established.Aim: To evaluate the agreement between aoSBP and aoPP values invasively and non-invasively obtained using different: 1) recording techniques (tonometry, oscilometry/plethysmography, ultrasound), 2) recording sites [radial, brachial (BA) and carotid artery (CCA)], 3) waveform analysis algorithms (e.g., direct analysis of the CCA pulse waveform vs. peripheral waveform analysis using general transfer functions, N-point moving average filters, etc.), 4) calibration schemes (systolic-diastolic calibration vs. methods using BA diastolic and mean blood pressure (bMBP); the latter calculated using different equations vs. measured directly by oscillometry, and 5) different equations to estimate bMBP (i.e., using a form factor of 33% (“033”), 41.2% (“0412”) or 33% corrected for heart rate (“033HR”).Methods: The invasive aortic (aoBP) and brachial pressure (bBP) (catheterization), and the non-invasive aoBP and bBP were simultaneously obtained in 34 subjects. Non-invasive aoBP levels were obtained using different techniques, analysis methods, recording sites, and calibration schemes.Results: 1) Overall, non-invasive approaches yielded lower aoSBP and aoPP levels than those recorded invasively. 2) aoSBP and aoPP determinations based on CCA recordings, followed by BA recordings, were those that yielded values closest to those recorded invasively. 3) The “033HR” and “0412” calibration schemes ensured the lowest mean error, and the “033” method determined aoBP levels furthest from those recorded invasively. 4) Most of the non-invasive approaches considered overestimated and underestimated aoSBP at low (i.e., 80 mmHg) and high (i.e., 180 mmHg) invasive aoSBP values, respectively. 5) The higher the invasively measured aoPP, the higher the level of underestimation provided by the non-invasive methods.Conclusion: The recording method and site, the mathematical method/model used to quantify aoSBP and aoPP, and to calibrate waveforms, are essential when estimating aoBP. Our study strongly emphasizes the need for methodological transparency and consensus for the non-invasive aoBP assessment.

Non-steady-state closed dynamic accumulation chambers are widely used to measure the respiration of terrestrial ecosystems, thanks to their low cost, low energy consumption and simple transportability, that allow measurements even in hostile and remote environments. However, the assessment of the accuracy and precision associated with the measurement system (independently of possible disturbances due to chamber-soil interactions) is rarely reported. This information is instead necessary for basic quality control, to compare data obtained by different devices and regression models and to provide Confidence Intervals (CIs) on the carbon flux values. This study quantifies the uncertainty associated with emission flux measurements, with a focus on very low fluxes. Calibration tests using different accumulation chambers and CO2 sensors were performed, and fluxes were calculated by means of different models (parametric, non-parametric and flux models). The results of this work show that the linear regression model has the best reproducibility when compared to the other tested models, regardless of the sensor used and the chamber volumes, while the second order polynomial regression has the best accuracy. We remark the importance of building a calibration curve in the range of the expected flux values, with an interval between the lowest and highest imposed flux that should not exceed two orders of magnitude. To evaluate the reproducibility of the measurement, performing replicates for each imposed flux value is essential. We also show that it is necessary to carefully identify the best time interval for interpolating the CO2 concentration curve in order to guarantee reproducibility and accuracy in flux estimates.

A collinear backscattering Mueller matrix (CBMM) imaging system has clear advantages in the detection of bulk biological tissues, which are highly scattering and depolarizing. Due to the double-pass configuration and noise in the system, the calibration of a collinear backscattering Mueller matrix imaging system is usually complex and of poor accuracy. In this work, we propose an alternative modified eigenvalue calibration method (ECM) based on the equivalent standard sample. For better noise suppression and higher calibration accuracy, we design the distribution of polarization states over the Poincaré sphere and solve for the parameters of equivalent standard samples by means of an optimization. Compared to other variants of the eigenvalue calibration method used in the double-pass system, the accuracy of the proposed method is improved by more than 40 times. The comparison results with the error model-based calibration methods indicate that the modified eigenvalue calibration method generally gives the best accuracy and precision, as well as the best reliability.

BackgroundEsophageal pressure (Pes) can be used as a reliable surrogate for pleural pressure, especially in critically ill patients requiring personalized mechanical ventilation strategies. How to choose the proper esophageal balloon filling volume and then find the optimal value of esophageal pressure remains a challenge. The study aimed to assess the feasibility of catheters for Pes monitoring in mechanically ventilated patients.Materials and methodsTwelve patients under pressure-controlled mechanical ventilation were included in this study. Raw esophageal pressure was recorded at different balloon filling volumes. Then, the P-V curves were determined. VWORK was the intermediate linear section on the end-expiratory P-V curve, and VBEST was the filling volume providing the maximum difference between Pes at end-inspiration and end-expiration. The raw value of Pes was recorded, and the calibrated values of Pes were calculated by calculating the esophageal wall pressure (Pew) and esophageal elastance (Ees).ResultsTwenty-four series of Pes measurements were performed. The mean VMIN and VMAX were 2.17 ± 0.49 ml (range, 1.0–3.0 ml) and 6.79 ± 0.83 ml (range, 5.0–9.0 ml), respectively, whereas VBEST was 4.69 ± 0.16 ml (range, 2.0–8.0 ml). Ees was 1.35 ± 0.51 cm H2O/ml (range, 0.26–2.38 cm H2O/ml). The estimated Pew at VBEST was 3.16 ± 2.19 cm H2O (range, 0–7.97 cm H2O). Patients with a body mass index (BMI) ≥ 25 kg/m2 had a significantly lower VMAX (5.88 [5.25–6] vs. 7.25 [7–8] ml, p = 0.006) and a significantly lower VBEST (3.69 [2.5–4.38] vs. 5.19 [4–6] ml, p = 0.036) than patients with a BMI < 25 kg/m2. Patients with positive end-expiratory pressure (PEEP) ≥ 10 cm H2O had a lower VMIN and VBEST than patients with PEEP < 10 cm H2O, P > 0.05. Patients in the supine position had a higher esophageal pressure than those in the prone position with the same balloon filling volume.ConclusionsCalibration of esophageal pressure to identify the best filling volume of esophageal balloon catheters is feasible. The esophageal pressure can be influenced by BMI, PEEP, and position. It is necessary to titrate the optimal inflation volume again when the PEEP values or the positions change.

The goal of this study is to provide a universally applicable procedure for a systematic evaluation of in situ measured data from single sensors regarding quantifying the uncertainty of the measurement results. As determining uncertainty for an environmental parameter also depends on the parameter itself, the focus here will be set on the variable water temperature in the first place. A separate analysis for salinity and other data will follow in later publications. With this first of a series of planned manuscripts on different parameters, we aim at providing a common understanding of how measurement uncertainty on single sensor measurements can be derived. Using an experimental in situ set-up with 6 different standard CTD sensors of two different brands, we created a four month-long, high-quality data set to be used to develop a reliable method for quantifying measurement uncertainties. Although the CTDs were deployed in a mooring in a coastal environment the described method can be extended to other deployment configurations as well. The described procedures have evolved as a stepwise process that takes the different perspectives of the involved authors into account, as well as the special conditions for environmental measurements, which are collected while the observed volume/area is undergoing a constant change. By sharing the ideas with other stakeholders, the basic concept can be extended to other observing programs and to other essential ocean variables.

The sound velocity profile is the base of various underwater acoustic equipment. In this paper, a low-cost sound velocity profiler is designed based on the time difference method. It mainly includes three parts: the control unit, the storage module and the ultrasonic measurement module. Its overall volume is small, and the standby power consumption is low. It can be integrated into various underwater measurement platforms and profilers to realize the sound velocity measurement, and it also could be used as a self-contained sound velocity sensor. Furthermore, according to the sound velocity measurement principle and response characteristics, a calibration algorithm based on Recurrent Neural Network (RNN) and Discrete Wavelet Transformation (DWT) is proposed, which can improve the accuracy and adapt to the nonlinear response of the system by using multiple sets of time data obtained from the measurements. It is verified by calibration experiments that the neural network calibration algorithm can effectively reduce the nonlinear system error in the measurement, and its effect is better than the traditional linear regression method. The designed system prototype can achieve measurement accuracy of 0.05m/s after calibration, which can meet the needs of low-cost and high-precision underwater sound velocity measurement.

The Atacama Large Millimeter-submillimeter Array (ALMA), sited on the high desert plains of Chajnantor in Chile, has opened a new window onto solar physics in 2016 by providing continuum observations at millimeter and sub-millimeter wavelengths with an angular resolution comparable to that available at optical (O), ultraviolet (UV), extreme ultraviolet (EUV), and X-ray wavelengths, and with superior time resolution. In the intervening years, progress has been made testing and commissioning new observing modes and capabilities, in developing data calibration strategies, and in data imaging and restoration techniques. Here we review ALMA current solar observing capabilities, the process by which a user may propose to use the instrument, and summarize the observing process and work flow. We then discuss some of the challenges users may encounter in imaging and analyzing their data. We conclude with a discussion of additional solar observing capabilities and modes under consideration that are intended to further exploit the unique spectral coverage provided by ALMA.

Background and purposeStudies showed that patients with hemorrhagic stroke are at a higher risk of developing deep vein thrombosis (DVT) than those with ischemic stroke. We aimed to develop a risk score (intracerebral hemorrhage-associated deep vein thrombosis score, ICH-DVT) for predicting in-hospital DVT after ICH.MethodsThe ICH-DVT was developed based on the Beijing Registration of Intracerebral Hemorrhage, in which eligible patients were randomly divided into derivation (60%) and internal validation cohorts (40%). External validation was performed using the iMCAS study (In-hospital Medical Complication after Acute Stroke). Independent predictors of in-hospital DVT after ICH were obtained using multivariable logistic regression, and β-coefficients were used to generate a scoring system of the ICH-DVT. The area under the receiver operating characteristic curve (AUROC) and the Hosmer–Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively.ResultsThe overall in-hospital DVT after ICH was 6.3%, 6.0%, and 5.7% in the derivation (n = 1,309), internal validation (n = 655), and external validation (n = 314) cohorts, respectively. A 31-point ICH-DVT was developed from the set of independent predictors including age, hematoma volume, subarachnoid extension, pneumonia, gastrointestinal bleeding, and length of hospitalization. The ICH-DVT showed good discrimination (AUROC) in the derivation (0.81; 95%CI = 0.79–0.83), internal validation (0.83, 95%CI = 0.80–0.86), and external validation (0.88; 95%CI = 0.84–0.92) cohorts. The ICH-DVT was well calibrated (Hosmer–Lemeshow test) in the derivation (P = 0.53), internal validation (P = 0.38), and external validation (P = 0.06) cohorts.ConclusionThe ICH-DVT is a valid grading scale for predicting in-hospital DVT after ICH. Further studies on the effect of the ICH-DVT on clinical outcomes after ICH are warranted.

Photoacoustic (PA) signals are typically broadband in nature. The bandwidth of PA signals depends on the size distribution of the underlying chromophores. Typically, conventional ultrasound (US) transducers, designed for pulse-echo imaging, have limited bandwidth, which reduces their sensitivity to the broadband PA signal. The rejection of out-of-band signals impairs image reconstruction, leading to the loss of image details. Visualization of biological structures, in particular deep targets with a range of sizes requires large acquisition bandwidth. In this work, we combine PA data acquired with two conventional US array probes with complementary frequency bands in order to widen the bandwidth. However, the two conventional transducers also differ in sensitivity and combining the data results in misrepresentation of PA signal strengths. Therefore, in this article we report a novel PA-based method to calibrate the relative sensitivities of the transducers. The proposed method was applied in various scenarios, including imaging vascular structures in vivo. Results revealed that it is feasible to visualize targets varying widely in sizes while combining complementary information acquired with dual US transducers. In addition, the application of sensitivity compensation ratios avoids misrepresentation in the imaging scheme by accounting for sensitivity differences of both transducers during image acquisition.

In this study, a deep learning method named U-net neural network is utilized to calibrate the gridded forecast of surface air temperature from the Global Ensemble Forecasting System (GEFS), with forecast lead times of 1–7 days in Xinjiang. The calibration performance of U-net is compared with three conventional postprocessing methods: unary linear regression (ULR), the decaying averaging method (DAM) and Quantile Mapping (QM). Results show that biases of the raw GEFS forecasts are mainly distributed in the Altai Mountains, the Junggar Basin, the Tarim Basin and the Kunlun Mountains. The four postprocessing methods effectively improve the forecast skills for all lead times, whereas U-net shows the best correction performance with the lowest mean absolute error (MAE) and the highest hit rate of 2°C (HR2) and pattern correlation coefficient (PCC). The U-net model considerably reduces the warm biases of the raw forecasts. The skill improvement magnitudes are greater in southern than northern Xinjiang, showing a higher mean absolute error skill score (MAESS). Furthermore, in order to distinguish the error sources of each forecasting scheme and to reveal their capabilities of calibrating errors of different sources, the error decomposition analysis is carried out based on the mean square errors. It shows that the bias term is the leading source of error in the raw forecasts, and barely changes as the lead time increases, which is mainly distributed in Tarim Basin and Kunlun Mountains. All four forecast calibrations effectively reduce the bias and distribution error of the raw forecasts, but only the U-net significantly reduces the sequence error.

Agent-based models have been developed and widely employed to assess the impact of disturbances or conservation management on animal habitat use, population development, and viability. However, the direct impacts of canopy disturbance on the arboreal movement of individual primates have been less studied. Such impacts could shed light on the cascading effects of disturbances on animal health and fitness. Orangutans are an arboreal primate that commonly encounters habitat quality deterioration due to land-use changes and related disturbances such as forest fires. Forest disturbance may, therefore, create a complex stress scenario threatening orangutan populations. Due to forest disturbances, orangutans may adapt to employ more terrestrial, as opposed to arboreal, movements potentially prolonging the search for fruiting and nesting trees. In turn, this may lead to changes in daily activity patterns (i.e., time spent traveling, feeding, and resting) and available energy budget, potentially decreasing the orangutan's fitness. We developed the agent-based simulation model BORNEO (arBOReal aNimal movEment mOdel), which explicitly describes both orangutans' arboreal and terrestrial movement in a forest habitat, depending on distances between trees and canopy structures. Orangutans in the model perform activities with a motivation to balance energy intake and expenditure through locomotion. We tested the model using forest inventory data obtained in Sebangau National Park, Central Kalimantan, Indonesia. This allowed us to construct virtual forests with real characteristics including tree connectivity, thus creating the potential to expand the environmental settings for simulation experiments. In order to parameterize the energy related processes of the orangutans described in the model, we applied a computationally intensive evolutionary algorithm and evaluated the simulation results against observed behavioral patterns of orangutans. Both the simulated variability and proportion of activity budgets including feeding, resting, and traveling time for female and male orangutans confirmed the suitability of the model for its purpose. We used the calibrated model to compare the activity patterns and energy budgets of orangutans in both natural and disturbed forests . The results confirm field observations that orangutans in the disturbed forest are more likely to experience deficit energy balance due to traveling to the detriment of feeding time. Such imbalance is more pronounced in males than in females. The finding of a threshold of forest disturbances that affects a significant change in activity and energy budgets suggests potential threats to the orangutan population. Our study introduces the first agent-based model describing the arboreal movement of primates that can serve as a tool to investigate the direct impact of forest changes and disturbances on the behavior of species such as orangutans. Moreover, it demonstrates the suitability of high-performance computing to optimize the calibration of complex agent-based models describing animal behavior at a fine spatio-temporal scale (1-m and 1-s granularity).

By using the normal and tangential forces measured in experiments of granular flow impact against a retaining wall, the effective interface friction angle of the wall was calculated in the dynamic phase and the static phase respectively, designated as δec and δes, and both of them are smaller than the interface friction angle measured by laboratory test (δ2). It is found δec shows an increasing trend against inclination angle (β), which is opposite to that of δes; and δec has an underestimation of effective interface friction angle. By comparing the calculated normal forces of static phase and of dynamic phase with the measured values, it is found the lower bound relation between δes/δ2 and β obtained from regression analysis could well calibrate the effective interface friction angle for both the wall and flume base. It is also noted that the increasing trend of δec against β may be explained by the higher proportion of gravity-friction force at lower inclination angle, i.e., the higher proportion of drag force at higher inclination angle.

Assessing tidal volume during mechanical ventilation is critical to improving gas exchange while avoiding ventilator-induced lung injury. Conventional flow and volume measurements are usually carried out by built-in pneumotachographs in the ventilator or by stand-alone flowmeters. Such flow/volume measurement devices are expensive and thus usually unaffordable in low-resource settings. Here, we aimed to design and test low-cost and technically-simple calibration and assembly pneumotachographs. The proposed pneumotachographs are made by manual perforation of a plate with a domestic drill. Their pressure-volume relationship is characterized by a quadratic equation with parameters that can be tailored by the number and diameter of the perforations. We show that the calibration parameters of the pneumotachographs can be measured through two maneuvers with a conventional resuscitation bag and by assessing the maneuver volumes with a cheap and straightforward water displacement setting. We assessed the performance of the simplified low-cost pneumotachographs to measure flow/volume during mechanical ventilation as carried out under typical conditions in low-resource settings, i.e., lacking gold standard expensive devices. Under realistic mechanical ventilation settings (pressure- and volume-control; 200–600 mL), inspiratory tidal volume was accurately measured (errors of 2.1% on average and

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R2 = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

LIDAR is an excellent means to obtain the information of buildings, forests, bridges, tunnels and many other big scenes, but the high price of 3D LIDAR currently limits its further application. To meet this challenge a mobile 3D imaging system based on 2D LIDAR is proposed. The system has the characteristics of large imaging range and low cost. The composition and implementation principle of each module of the system are introduced in detail and a calibration method for the 3-axis assembly error is proposed. In this method Levenberg-Marquardt (LM) optimization algorithm is used to obtain the optimal value of the 2D LIDAR 3-axis attitude angle, which is used to compensate for the point cloud distortion caused by the assembly error. The experimental results show that the proposed method can effectively reduce the point cloud distortion caused by assembly error. This system can meet the application demand of big scenes 3D imaging.

The multi-armed bandit (MAB) problem models a decision-maker that optimizes its actions based on current and acquired new knowledge to maximize its reward. This type of online decision is prominent in many procedures of Brain-Computer Interfaces (BCIs) and MAB has previously been used to investigate, e.g., what mental commands to use to optimize BCI performance. However, MAB optimization in the context of BCI is still relatively unexplored, even though it has the potential to improve BCI performance during both calibration and real-time implementation. Therefore, this review aims to further describe the fruitful area of MABs to the BCI community. The review includes a background on MAB problems and standard solution methods, and interpretations related to BCI systems. Moreover, it includes state-of-the-art concepts of MAB in BCI and suggestions for future research.

ObjectiveTo assess the performance of the Pediatric Risk of Mortality III (PRISM III) and Pediatric Index of Mortality III (PIM III) indices in a tertiary pediatric intensive care unit (PICU) in Saudi Arabia and to identify the factors affecting the observed performance.DesignRetrospective, single-center study using data collected from the Virtual Pediatric Systems web-based database.SettingKing Fahad Medical City PICU, Saudi Arabia.PatientsAll pediatric patients

This paper summarizes the results from the system level test campaign of the Engineering Test Unit (ETU) of the ‘Ocean Color Instrument’ (OCI), the primary payload of NASA’s ‘Plankton, Aerosol, Cloud and ocean Ecosystem’ (PACE) mission. The main goals of the test campaign were to optimize characterization procedures and evaluate system level performance relative to model predictions. Critical performance parameters such as radiometric gain, signal-to-noise ratio, polarization, instantaneous field-of-view, temperature sensitivity, relative spectral response and stability were evaluated for wavelengths from 600 to 2,260 nm and are in line with expectations. We expect the OCI flight unit to meet the PACE mission performance requirements. Building and testing the ETU has been extremely important for the development of the OCI flight unit (e.g. improved SNR by increasing the aperture, optimized thermal design), and we strongly recommend the inclusion of an ETU in the development of future spaceborne sensors that rely on novel technological designs. ETU testing led to the discovery of a hysteresis issue with the SWIR bands, and a correction algorithm was developed. Also, the coregistration of the SWIR bands relative to each other is worse than expected, but this was discovered too late in the schedule to remediate.

Lidar is a reliable tool for active remote sensing detection of atmospheric aerosols. A multi-wavelength aerosol lidar (MWAL) with 355 nm, 532 and 1064 nm as detection light sources has been developed and deployed for operational observations at Haidian District Meteorological Service of Beijing. The structure design, specifications, observation campaign, and detection principle of the MWAL are introduced. To ensure the accuracy and reliability of the lidar observation data, the calibration contents, and methods of lidar are proposed, including the correction, and gluing of the original data, the collimation of the transmitting and receiving optical axes, the testing of signal saturation, the correction of molecular Rayleigh fitting and the determination of the depolarization ratio correction factor. Finally, a haze process from 29 September to 2 October 2019 was observed and analyzed using the data of lidar, digital radiosonde, air quality and relative humidity observed by the Haidian District Meteorological Service. The detection results show the reliability of lidar which can effectively obtain the temporal and spatial variation characteristics of the haze. The profiles of aerosol extinction coefficient, potential temperature and relative humidity can be effectively used to analyze the haze thickness and the influence of relative humidity on aerosol particles. The data of air quality monitor shows that PM10 is the main pollutant and the ratio of PM2.5/PM10 is negatively correlated with relative humidity. Finally, the HYSPLIT trajectory tracking model of the National Oceanic and Atmospheric Administration (NOAA) is used to further study the source of pollutants in this haze process.

Background: We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making.Methods: We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework.Results: The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of −0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $653 and $685, respectively, at a willingness-to-pay (WTP) threshold of $66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters’ posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $809 at the same WTP threshold.Conclusion: Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty.

Identifying white matter (WM) tracts to locate eloquent areas for preoperative surgical planning is a challenging task. Manual WM tract annotations are often used but they are time-consuming, suffer from inter- and intra-rater variability, and noise intrinsic to diffusion MRI may make manual interpretation difficult. As a result, in clinical practice direct electrical stimulation is necessary to precisely locate WM tracts during surgery. A measure of WM tract segmentation unreliability could be important to guide surgical planning and operations. In this study, we use deep learning to perform reliable tract segmentation in combination with uncertainty quantification to measure segmentation unreliability. We use a 3D U-Net to segment white matter tracts. We then estimate model and data uncertainty using test time dropout and test time augmentation, respectively. We use a volume-based calibration approach to compute representative predicted probabilities from the estimated uncertainties. In our findings, we obtain a Dice of ≈0.82 which is comparable to the state-of-the-art for multi-label segmentation and Hausdorff distance

Computational models of the electrical potential across a cell membrane are longstanding and vital tools in electrophysiology research and applications. These models describe how ionic currents, internal fluxes, and buffering interact to determine membrane voltage and form action potentials (APs). Although this relationship is usually expressed as a differential equation, previous studies have shown it can be rewritten in an algebraic form, allowing direct calculation of membrane voltage. Rewriting in this form requires the introduction of a new parameter, called Γ0 in this manuscript, which represents the net concentration of all charges that influence membrane voltage but are not considered in the model. Although several studies have examined the impact of Γ0 on long-term stability and drift in model predictions, there has been little examination of its effects on model predictions, particularly when a model is refit to new data. In this study, we illustrate how Γ0 affects important physiological properties such as action potential duration restitution, and examine the effects of (in)correctly specifying Γ0 during model calibration. We show that, although physiologically plausible, the range of concentrations used in popular models leads to orders of magnitude differences in Γ0, which can lead to very different model predictions. In model calibration, we find that using an incorrect value of Γ0 can lead to biased estimates of the inferred parameters, but that the predictive power of these models can be restored by fitting Γ0 as a separate parameter. These results show the value of making Γ0 explicit in model formulations, as it forces modellers and experimenters to consider the effects of uncertainty and potential discrepancy in initial concentrations upon model predictions.

The DSCOVR mission was designed to take advantage of the first Lagrangian position (L1) to continuously observe the Earth sunlit disk. To facilitate EPIC V03 data product validation and fusion, the EPIC V03 navigation and calibration stability is assessed. The Aqua-MODIS, NPP-VIIRS, and N20-VIIRS based radiometric scaling factors are also provided. The V03 navigation error was 15.5 km, a 50% improvement over V02 and within what can be achieved by an objective image alignment algorithm. Both the navigation accuracy and precision were improved in V03 and were found to be comparable across all EPIC visible channels. The all-sky tropical ocean and deep convective cloud ray-matched MODIS- and VIIRS-referenced EPIC inter-calibration gains are within 0.4% of one-another, and are also within 0.4% of a previous study’s NPP-VIIRS-referenced gains. The inter-calibration study reveals that EPIC bands 5 and 6 degraded mostly within the first year of operation and becoming stable thereafter, whereas bands 7 and 10 were stable during the 6-years record. The capability of the V03 navigation allowed EPIC stability to be monitored using DCC and Libya-4 invariant targets. The EPIC V03 calibration was mostly stable within 0.3% over the 6-years record, as determined from inter-calibration and invariant target monitoring methods. Remarkably, both the DCC- and Libya-4-based methods were able to confirm the stability of the E-8 and E-9 oxygen absorptions—a stability comparable to that of the E-7 and E-10 reference bands. No significant change in the navigation accuracy or calibration stability was observed after the DSCOVR 2019 safe mode incident. The impressive stability of the DSCOVR EPIC L1B V03 channel radiances can greatly benefit the Earth remote sensing community by providing diurnally complete daytime radiative flux and environmental retrievals for future sensors located at L1.

Bronchopulmonary dysplasia (BPD) is the most prevalent and clinically significant complication of prematurity. Accurate identification of at-risk infants would enable ongoing intervention to improve outcomes. Although postnatal exposures are known to affect an infant's likelihood of developing BPD, most existing BPD prediction models do not allow risk to be evaluated at different time points, and/or are not suitable for use in ethno-diverse populations. A comprehensive approach to developing clinical prediction models avoids assumptions as to which method will yield the optimal results by testing multiple algorithms/models. We compared the performance of machine learning and logistic regression models in predicting BPD/death. Our main cohort included infants

Background: There are scarce and controversial data on whether human immunodeficiency virus (HIV) infection is associated with changes in aortic pressure (aoBP) and waveform-derived indexes. Moreover, it remains unknown whether potential differences in aoBP and waveform indexes between people living with HIV (PLWHIV) and subjects without HIV (HIV-) would be affected by the calibration method of the pressure waveform.Aims: To determine: (i) whether PLWHIV present differences in aoBP and waveform-derived indexes compared to HIV- subjects; (ii) the relative impact of both HIV infection and cardiovascular risk factors (CRFs) on aoBP and waveform-derived indexes; (iii) whether the results of the first and second aims are affected by the calibration method.Methods: Three groups were included: (i) PLWHIV (n = 86), (ii) HIV- subjects (general population; n = 1,000) and (iii) a Reference Group (healthy, non-exposed to CRFs; n = 398). Haemodynamic parameters, brachial pressure (baBP; systolic: baSBP; diastolic: baDBP; mean oscillometric: baMBPosc) and aoBP and waveform-derived indexes were obtained. Brachial mean calculated (baMBPcalc=baDBP+[baSBP-baDBP]/3) pressure was quantified. Three waveform calibration schemes were used: systolic-diastolic, calculated (baMBPcalc/baDBP) and oscillometric mean (baMBPosc/baDBP).Results: Regardless of CRFs and baBP, PLWHIV presented a tendency of having lower aoBP and waveform-derived indexes which clearly reached statistical significance when using the baMBPosc/baDBP or baMBPcalc/baDBP calibration. HIV status exceeded the relative weight of other CRFs as explanatory variables, being the main explanatory variable for variations in central hemodynamics when using the baMBPosc/baDBP, followed by the baMBPcalc/baDBP calibration.Conclusions: The peripheral waveform calibration approach is an important determinant to reveal differences in central hemodynamics in PLWHIV.

In the past years, the gamma-ray detector designs based on the monolithic crystals have demonstrated to be excellent candidates for the design of high-performance PET systems. The monolithic crystals allow to achieve the intrinsic detector resolutions well below state-of-the-art; to increase packing fraction thus, increasing the system sensitivity; and to improve lesion detectability at the edges of the scanner field of view (FOV) because of their intrinsic depth of interaction (DOI) capabilities. The bottleneck to translate to the clinical PET systems based on a large number of monolithic detectors is eventually the requirement of mechanically complex and time-consuming calibration processes. To mitigate this drawback, several methods have been already proposed, such as using non-physically collimated radioactive sources or implementing the neuronal networks (NN) algorithms trained with simulated data. In this work, we aimed to simplify and fasten a calibration process of the monolithic based systems. The Normal procedure consists of individually acquiring a 11 × 11 22Na source array for all the detectors composing the PET system and obtaining the calibration map for each module using a method based on the Voronoi diagrams. Two reducing time methodologies are presented: (i) TEST1, where the calibration map of one detector is estimated and shared among all others, and (ii) TEST2, where the calibration map is slightly modified for each module as a function of their detector uniformity map. The experimental data from a dedicated prostate PET system was used to compare the standard calibration procedure with both the proposed methods. A greater similarity was exhibited between the TEST2 methodology and the Normal procedure; obtaining spatial resolution variances within 0.1 mm error bars and count rate deviations as small as 0.2%. Moreover, the negligible reconstructed image differences (13% deviation at most in the contrast-to-noise ratio) and almost identical contrast values were reported. Therefore, this proposed method allows us to calibrate the PET systems based on the monolithic crystals reducing the calibration time by approximately 80% compared with the Normal procedure.

The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project has deployed 194 profiling floats equipped with biogeochemical (BGC) sensors, making it one of the largest contributors to global BGC-Argo. Post-deployment quality control (QC) of float-based oxygen, nitrate, and pH data is a crucial step in the processing and dissemination of such data, as in situ chemical sensors remain in early stages of development. In situ calibration of chemical sensors on profiling floats using atmospheric reanalysis and empirical algorithms can bring accuracy to within 3 μmol O2 kg–1, 0.5 μmol NO3– kg–1, and 0.007 pH units. Routine QC efforts utilizing these methods can be conducted manually through visual inspection of data to assess sensor drifts and offsets, but more automated processes are preferred to support the growing number of BGC floats and reduce subjectivity among delayed-mode operators. Here we present a methodology and accompanying software designed to easily visualize float data against select reference datasets and assess QC adjustments within a quantitative framework. The software is intended for global use and has been used successfully in the post-deployment calibration and QC of over 250 BGC floats, including all floats within the SOCCOM array. Results from validation of the proposed methodology are also presented which help to verify the quality of the data adjustments through time.

In an electroencephalogram- (EEG-) based brain–computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.

Catching a ball in a parabolic flight is a complex task in which the time and area of interception are strongly coupled, making interception possible for a short period. Although this makes the estimation of time-to-contact (TTC) from visual information in parabolic trajectories very useful, previous attempts to explain our precision in interceptive tasks circumvent the need to estimate TTC to guide our action. Obtaining TTC from optical variables alone in parabolic trajectories would imply very complex transformations from 2D retinal images to a 3D layout. We propose based on previous work and show by using simulations that exploiting prior distributions of gravity and known physical size makes these transformations much simpler, enabling predictive capacities from minimal early visual information. Optical information is inherently ambiguous, and therefore, it is necessary to explain how these prior distributions generate predictions. Here is where the role of prior information comes into play: it could help to interpret and calibrate visual information to yield meaningful predictions of the remaining TTC. The objective of this work is: (1) to describe the primary sources of information available to the observer in parabolic trajectories; (2) unveil how prior information can be used to disambiguate the sources of visual information within a Bayesian encoding-decoding framework; (3) show that such predictions might be robust against complex dynamic environments; and (4) indicate future lines of research to scrutinize the role of prior knowledge calibrating visual information and prediction for action control.

IntroductionMetabolic simulators (MS) produce simulated human breaths for the purpose of verification of cardiopulmonary exercise test (CPET) equipment. MS should produce consistent identical breaths with known CO2 and O2 gas concentrations over a range of breath rates and tidal volumes. Reliability of a CPET metabolic cart depends on ongoing quality control and maintenance of the device, including intermittent verification with a MS. We compared two MS devices against two standard CPET systems.MethodsThe Vacumed 17056 (Vacumetrics, Ventura, CA) and Relitech (Relitech Systems BV, Nijkerk, The Netherlands) were used with two standard metabolic carts (Vyntus CPX and Vyntus ONE, both Vyaire Medical, Mettawa, IL, United States). Tidal volume (VT) was set at 2 and 3 L and breathing frequency ranged from 20 to 80 breaths per minute for each MS. At each set point, we measured three sets of 40 breaths. Primary outcome parameters collected were VT, oxygen consumption (v.O2), carbon dioxide production (v.CO2), and respiratory exchange ratio (RER).ResultsVT, RER, v.O2, and v.CO2 results as obtained from both MS were all within the limits of acceptability, at both tidal volume settings, and all ventilatory rates. No significant trends were identified for either MS device. The Relitech MS produced tidal volumes that were closer to the target VT for both CPET carts at both VT and all rates, but the results of both MS were within acceptable ranges.ConclusionVerification of CPET equipment using either the VM or RT metabolic simulator, producing highly accurate and predictable simulated breaths of known composition, enabling CPET laboratory managers to rely on subject test data obtained during cardiopulmonary exercise testing.