Bias Values Research Articles

SIMS matrix effects in oxygen isotope analysis of olivine and pyroxene: Application to Acfer 094 chondrite chondrules and reconsideration of the primitive chondrule minerals (PCM) line

The instrumental bias (here expressed as bias*, the difference relative to San Carlos Olivine, SC-Ol) of oxygen isotopes in secondary ion mass spectrometry (SIMS) analyses of olivine and pyroxene were investigated. Sixteen olivine (Fo0–100), nine orthopyroxene (En70–100Wo0–3), and three clinopyroxene (En28-49Wo45–50) reference materials (RMs) were utilized. The values of bias* are nearly invariant (within 0 ± 0.5‰) among magnesian olivine (Fo ≥60) RMs but decrease systematically with increasing Fe molar fraction down to ~ −10‰ (Fo0). The session-to-session variability of bias* for Fo ≥60 olivines is ≤0.3‰ but becomes slightly larger for more Fe-rich compositions (≤1.5‰). Orthopyroxene RMs have a narrow range of bias* values (−1.7‰ to −2.6‰) that show session-to-session variability ≤0.3‰; correspondingly, the sputter rates are similarly low and resemble those of Fo ≥60 olivines. Clinopyroxene RMs bias* values (−0.2‰ to +1.2‰) are more variable and have larger session-to-session variability (≤0.8‰), which appear to be independent of sputter rates. Bias* of the same RMs change slightly from session to session with different analytical settings (≤0.6‰ for Fo ≥60 and as much as 2‰ for fayalite) so that equations of bias* as a function of Fo, En, and Wo should be determined for each SIMS session by the analyses of multiple RMs.The new suite of olivine and pyroxene RMs with expanded composition range were used to correct the instrumental biases during oxygen three-isotope analysis of olivine and low-Ca pyroxene in chondrules of the ungrouped Acfer 094 chondrite, from which the primitive chondrule minerals (PCM, δ18O vs. δ17O) line was originally derived. The PCM line is considered to be a mixing trend of two extreme primary oxygen isotope reservoirs of solids in the early solar system. However, this new dataset, with better analytical precision and instrumental bias correction, allows two oxygen isotope trends to be clearly identified. One is represented by chondrules that plot on or above the PCM line, likely linked to ordinary chondrite (OC)-like materials. A second trend is represented by the major chondrule population in this chondrite that defines a regression line of δ17O = (0.968 ± 0.022) × δ18O − (3.46 ± 0.10) (MSWD = 2.0), consistent with recent oxygen isotope data of chondrules in carbonaceous chondrites (i.e., CV, CK, CO, and CM). We propose that this regression line represents a mixing of two oxygen isotope reservoirs (possibly 16O-rich solids and 16O-poor H2O ice) in the outer solar system and likely resulting from the separate evolutions of isotope reservoirs after the “Jupiter divide” built up.

Si integration of La0.7Sr0.3MnO3:BiFeO3 nanocomposite thin films with strong exchange bias coupling

Integrating functional oxide-based nanocomposite thin films on Si wafer is a large step toward their applications in electronics and spintronics. Here, to overcome the large lattice mismatch and potential inter-diffusion between the complex oxides and Si, a set of buffer layers of SrRuO3 (SRO)/TiN have been applied. The La0.7Sr0.3MnO3:BiFeO3 system has been selected to grow on the SRO/TiN buffered Si, and the nanocomposite films exhibit highly textured growth along the c direction for both phases. The microstructure study shows a typical vertically aligned nanocomposite structure of the films, which is similar to the same films on single crystal oxide substrates. Furthermore, magnetic measurements indicate the strong ferromagnetic–antiferromagnetic coupling in the nanocomposite system, which refers to the large exchange bias value HEB of ∼1800 Oe in the L0.5B0.5 sample under out-of-plane magnetic field. This work provides an effective approach for the Si integration of oxide-based nanocomposite thin films.

Phase-Shifting Common-Path Polarization Self-Interferometry for Evaluating the Reconstruction of Holograms Displayed on a Phase-Only Display

In this work we apply a phase-shifting interferometry (PSI) to evaluate the reconstruction of complex-valued holograms displayed onto a phase-only spatial light modulator (SLM). The interferometer is vibration-free since it uses a common-path polarization arrangement based on the SLM itself, which is used simultaneously to display the hologram and to apply the phase-shifting values. The change from the hologram display configuration to the interferometer configuration involves only a rotation of a polarizer. The continuous phase modulation provided by the SLM allows using PSI with arbitrary phase bias values. Several examples are demonstrated by generating different combinations of modes with an efficient on-axis hologram encoding technique.

Model Re-Estimation: An Alternative for Poor Predictive Performance during External Evaluations? Example of Gentamicin in Critically Ill Patients.

Background: An external evaluation is crucial before clinical applications; however, only a few gentamicin population pharmacokinetic (PopPK) models for critically ill patients included it in the model development. In this study, we aimed to evaluate gentamicin PopPK models developed for critically ill patients. Methods: The evaluated models were selected following a literature review on aminoglycoside PopPK models for critically ill patients. The data of patients were retrospectively collected from two Quebec hospitals, the external evaluation and model re-estimation were performed with NONMEM® (v7.5) and the population bias and imprecisions were estimated. Dosing regimens were simulated using the best performing model. Results: From the datasets of 39 and 48 patients from the two Quebec hospitals, none of the evaluated models presented acceptable values for bias and imprecision. Following model re-estimations, all models showed an acceptable predictive performance. An a priori dosing nomogram was developed with the best performing re-estimated model and was consistent based on recommended dosing regimens. Conclusion: Due to the poor predictive performance during the external evaluations, the latter must be prioritized during model development. Model re-estimation may be an alternative to developing a new model, especially when most known models display similar covariates.

Assessing for Bias and Evidential Value: Introducing the P-Curve to Social Work Research

High-quality research is essential for the creation and delivery of effective social welfare interventions and policies. Such research is a cornerstone of evidence-based practice and more generally for the cultivation of intra- and interdisciplinary knowledge. The ongoing “replication crisis” (Ioannidis, 2005; Yaffe, 2019) in the biomedical and social sciences suggests that social work’s evidence base may not be as robust as it appears. In other words, social work research may have suboptimal levels of false positive findings, bias, and irreproducibility, among other issues—which together undergird many aspects of unreplicable research. The replication crisis serves as an important reminder—that social work researchers must continuously assess the veracity of their findings (Howard & Garland, 2015) if they are to make professional, social, and epistemic progress. Indeed, the promotion and publication of poorly conducted research (Chambers, 2017; Dunleavy, 2020a, 2021), characterized in part by the selective reporting of results, p-hacking (i.e., continuing to collect or manipulating data until the associated result is statistically significant; see Head et al., 2015; Simonsohn et al., 2014), and publication bias (e.g., Chambers, 2017; Rosenthal, 1979; Simmons et al., 2011), can lead to the adoption of ineffective or harmful practices and undermine genuine advances in knowledge (Dunleavy, 2020b, 2021; Gambrill, 2011).

Improved bias value and new membership function to enhance the performance of fuzzy support vector Machine

The effect of an outlier in classification is a problem in the Fuzzy Support Vector Machine (FSVM). In this paper, two new methodologies have been implemented to compute fuzzy membership values for the fuzzy data to address this issue. The membership values that arrived through these methods have been inputted in a fuzzy support vector machine. A method has been devised to select the best bias value to bring out the classifier using the bias values obtained through the support vectors. The Geometric Mean (Gm) and consistency in the newly proposed method for selecting bias values are used as the criteria to choose the best bias value. The methods such as average bias value, individual bias value, weighted average bias value, and median bias value are considered for this purpose. The obtained classifier improves the classification accuracy by reducing the fuzziness in data. The relation between the new membership function and the slack variable ξi has been exposed in terms of how new membership function reduces the chances of ξi become a basic variable. An experiment has been conducted with seventeen datasets, including fourteen real-world datasets. A comparison of the performance of the proposed Membership function with other membership functions is made. The robustness of the proposed membership functions for outliers is verified. A statistical test has been performed to ensure the significance of the proposed model.

What Determines Active Region Coronal Plasma Composition?

The chemical composition of the solar corona is different from that of the solar photosphere, with the strongest variation being observed in active regions (ARs). Using data from the Extreme Ultraviolet (EUV) Imaging Spectrometer (EIS) on Hinode, we present a survey of coronal elemental composition as expressed in the first ionization potential (FIP) bias in 28 ARs of different ages and magnetic flux content, which are at different stages in their evolution. We find no correlation between the FIP bias of an AR and its total unsigned magnetic flux or age. However, there is a weak dependence of FIP bias on the evolutionary stage, decreasing from 1.9 to 2.2 in ARs with spots to 1.5–1.6 in ARs that are at more advanced stages of the decay phase. FIP bias shows an increasing trend with average magnetic flux density up to 200 G, but this trend does not continue at higher values. The FIP bias distribution within ARs has a spread between 0.4 and 1. The largest spread is observed in very dispersed ARs. We attribute this to a range of physical processes taking place in these ARs, including processes associated with filament channel formation. These findings indicate that, while some general trends can be observed, the processes influencing the composition of an AR are complex and specific to its evolution, magnetic configuration, or environment. The spread of FIP bias values in ARs shows a broad match with that previously observed in situ in the slow solar wind.

Magnetic rare-earth ion mediated 4f-3d interlayer coupling and giant exchange bias in single layered Ruddlesden-Popper perovskites SrLnCo0.5Mn0.5O4 (Ln = Pr, Nd)

We report an in-depth study on exotic magnetic nature including giant exchange bias in single layered Ruddlesden-Popper structure polycrystalline perovskites SrRCo0.5Mn0.5O4 (R = Pr, and Nd). X-ray diffraction, neutron diffraction, and Raman spectroscopic studies on both compounds confirm tetragonal crystal structure with I4/mmm space group. Temperature evolution of dc magnetization and ac susceptibility show multiple magnetic phase transitions in both the compounds, i.e., high temperature ferromagnetic transition (Tc = 178/172 K) followed by antiferromagnetic transition at an intermediate temperature (TN = 90/100 K) and finally entering to a glassy magnetic state at lower temperatures (Tg = 35/25 K). Neutron powder diffraction confirms the absence of long-range magnetic order in both compounds. Atomically disordered (Co/Mn)O2 layers give rise to intralayer short range 3d-3d magnetic interactions resulting in co-existence of ferromagnetic and antiferromagnetic regions producing frozen magnetic state below Tg. Present experimental results and analysis show giant exchange bias of − 3.6/− 1.7 kOe (Pr/Nd systems) under cooling field of 50 kOe at 5 K, which stems from the inherent magnetic inhomogeneity. Our results reveal that magnetic rare-earth ion (Pr/Nd) induces a pathway of interlayer 4f-3d magnetic interactions leading to antiferromagnetic interaction between adjacent (Co/Mn)O2 layers, and this has a significant influence on the exchange bias values at low temperature.

Advancement of Generalized Method of Moment Estimation (GMM) For Spatial Dynamic Panel Simultaneous Equations Models with Fixed Time Effect

This research paper aims to form and estimate the spatial dynamic panel simultaneous equations models (SDPS) with fixed time effect that potentially have heteroscedasticity cases. The model formed with the individual effect is not eliminated but placed in the error model to accommodate cases of heteroscedasticity in the model. GMM with two stages least square (2SLS) method for the single equation is deliberately chosen as the estimation method for the SDPS model because it can eliminate heterogeneity cases in the model. The effectiveness of the estimate is seen based on the value of RMSE (Root Mean Square Error), mean and standard deviation (SD) of bias estimate by simulating Monte Carlo 100 times with different parameter pairs and different pairs N and T can also be concluded that parameter scenario changes do not give much effect on the mean bias value and SD bias. The SDPS model shows that the consistency of the estimated parameter values can be achieved easily if the number of T is added. Changes in the number of N and T indicate that the greater the N and T, the smaller RMSE value tends to be.

Evaluation of the Design of "Shape" and "Meaning" of Book Binding from the Perspective of Deep Learning.

Book binding is the procedure of manually accumulating a book in codex format from a well-ordered pile of paper sheets, which are folded together into sections or occasionally left as a stack of individual sheets. The books undergo binding into different shapes and sizes. Numerous kinds of book bindings are available, each of which comes with its own merits and demerits. Some of them are highly durable, some of them are light-weight, and some of them are attractive. Therefore, it is needed to effectively identify and classify the shape and type of book bindings. With this motivation, this paper develops a butterfly optimization algorithm with a deep learning-enabled book binding classification (BOADL-BBC) model. The major intention of the BOADL-BBC technique is to identify and categorise three different types of book bindings from the input images, namely, hard binding, soft binding, and long-stitch binding. The proposed BOADL-BBC technique initially employs a DL-based Inception v3 model to derive useful feature vectors from the images. For effective classification of book bindings, the BOA with wavelet kernel extreme learning machine (WKELM) model can be applied. The weight and bias values involved in the WKELM model can be effectively adjusted by the use of BOA for book binding classification shows the novelty of the work. To ensure the enhanced performance of the BOADL-BBC technique, a series of simulations were carried out using a set of images that people collected on their own. The experimental results stated that the BOADL-BBC technique has obtained a maximum book binding classification accuracy of 95.56%.

Multivariate regression (MVR) and different artificial neural network (ANN) models developed for optical transparency of conductive polymer nanocomposite films

The present study addresses a comparative performance assessment of multivariate regression (MVR) and well-optimized feed-forward, generalized regression and radial basis function neural network models which aimed to predict transmitted light intensity (Itr) of carbon nanotube (CNT)-loaded polymer nanocomposite films by employing a large set of spectroscopic data collected from photon transmission measurements. To assess prediction performance of each developed model, universally accepted statistical error indices, regression, residual and Taylor diagram analyses were performed. As a novel performance evaluation criterion, 2D kernel density mapping was applied to predicted and experimental Itr data to visually map out where the correlations are stronger and which data points can be more precisely estimated using the studied models. Employing MVR analysis, empirical equation of Itr was acquired as a function of only four input elements due to sparseness and repetitive nature of the remaining input variables. Relative importance of each input variable was calculated separately through implementing Garson’s algorithm for the best ANN model and mass fraction of CNT nanofillers was found as the most significant input variable. Using interconnection weights and bias values obtained for feed-forward neural network (FFNN) model, a neural predictive formula was derived to model Itr in terms of all input variables. 2D kernel density maps computed for each ANN model have shown that correlations between measured data and ANN predicted values are stronger for a specific Itr range between 0% and 18%. To measure the stability of the ANN models, as a final analysis, 5-fold cross-validation method was applied to whole measurement data and 5 different iterations were additionally performed on each ANN model for 5 different training and test data splits. Statistical results found from 5-fold cross-validation analysis have reaffirmed that FFNN model exhibited outperformed prediction ability over all other ANN models and all FFNN predicted Itr values agreed well with experimental Itr data. Taken all computational results together, one can adapt our proposed FFNN model and neural predictive formula to predict Itr of polymer nanocomposite films, which can be made from different polymers and nanofillers, by considering specific data range as presented in this study with statistical details.

Computed tomographic evaluation of the acetabulum for age estimation in an Indian population using principal component analysis and regression models

The acetabulum presents as a well-preserved evidence, resistant to taphonomic degradation changes and can thus aid in the age estimation process. A CT-based examination of the acetabulum can further help simplify the process of age estimation by overcoming the time-consuming process of maceration and by doing away with the interference resulting from tissue remnants. The aim of the present study was to evaluate the role of the acetabulum for age estimation in an Indian population through a CT-based examination, using principal component analysis and regression models. CT images of 400 individuals aged 10years and above were evaluated according to the features defined in the San-Millán-Rissech method of age estimation. Five of the seven morphological features defined by San-Millán-Rissech were appreciable on CT scans, and, to enable further statistical analysis, a cumulative score was computed using these five features. A significant correlation of 0.835 and 0.830 for the right and left acetabulum, respectively, was obtained between computed cumulative scores and chronological age of individuals. No significant sex differences were observed in the scoring of different age-related morphological changes. Regression models were generated using individual features and cumulative scores. Regression models derived using the cumulative score yielded inaccuracy values of 9.67years for the right acetabulum and 9.15years for the left acetabulum. Inaccuracy and bias values were computed for each individual feature, as well as for each decade, using mean point ages established within the original study. Amongst the various features, acetabular rim porosity was seen to have the lowest values of inaccuracy (11.50years) and bias (2.32years) and activity on outer edge of acetabular fossa the highest (inaccuracy and bias values of 22.36years and 21.50years, respectively). Taking into consideration this differential contribution towards age estimation, weighted coefficients and mean point ages for different morphological features were determined using principal component analysis. Subsequently, summary age models were generated from the obtained weighted coefficients and mean age values. Summary age models derived in the present study yield lower estimates of inaccuracy of 7.60years for the right acetabulum and 7.82years for the left acetabulum. While regression models derived in the present study allow for age estimation using even a single appreciable feature, summary age models take into account the contribution of each feature and generate more accurate estimates of age. Both statistical computations yield reduced error rates and thus can render greater applicability to the acetabulum in forensic age estimation.

Capability and Robustness of Novel Hybridized Artificial Intelligence Technique for Sediment Yield Modeling in Godavari River, India

Suspended sediment yield (SSY) prediction plays a crucial role in the planning of water resource management and design. Accurate sediment prediction using conventional models is very difficult due to many complex processes. We developed a fully automatic highly generalized accurate and robust artificial intelligence models for SSY prediction in Godavari River Basin, India. The genetic algorithm (GA), hybridized with an artificial neural network (ANN) (GA-ANN), is a suitable artificial intelligence model for SSY prediction. The GA is used to concurrently optimize all ANN’s parameters. The GA-ANN was developed using daily water discharge, with water level as the input data to estimate the daily SSY at Polavaram, which is the farthest gauging station in the downstream of the Godavari River Basin. The performances of the GA-ANN model were evaluated by comparing with ANN, sediment rating curve (SRC) and multiple linear regression (MLR) models. It is observed that the GA-ANN contains the highest correlation coefficient (0.927) and lowest root mean square error (0.053) along with lowest biased (0.020) values among all the comparative models. The GA-ANN model is the most suitable substitute over traditional models for SSY prediction. The hybrid GA-ANN can be recommended for estimating the SSY due to comparatively superior performance and simplicity of applications.

MO-QoE: Video QoE using multi-feature fusion based Optimized Learning Models

The escalating demand for video content and streaming services has made it a predominant medium of exchanging information in the modern era. Videos are processed, compressed, and streamed over dynamic wireless channels having limited bandwidth. This introduces video impairments that deteriorate the video quality. Humans are the targeted observers of multimedia content. Improving viewing experience requires human intervened subjective assessment of video quality; which is relatively cumbersome and resource intensive. Devising alternatives that can estimate subjective quality scores are essential to evaluate Quality-of-Experience (QoE) in multimedia applications and services. The features (objective quality metrics and impairment factors) provide a measure of artifacts and content characteristics that influence observers viewing experience. We have proposed MO-QoE, a framework to predict video QoE using Multi-Feature Fusion (MFF) based Optimized Learning Models (OLMs). The OLMs are the optimized neural network models designed for QoE prediction. We make use of Adaptive Moment estimation and Batch Gradient Descent algorithms that update the values of weight and biases of the learning models to their optimal values. The inputs to our model is based on MFF scheme, that eliminates distortion specific biased performance of any individual feature. The resultant non-linear regression score from the model is obtained after fusion of scores from multiple features. The QoE prediction using the MFF-based OLMs has been tested on publicly available databases. The prediction performance/accuracy is improved by ≈2 to 3 times as compared to the existing models.

Undeserved reward but not inevitable loss biases attention: Personal control moderates evaluative attentional biases in the additional-singleton paradigm.

It is important for organisms to notice signals of opportunities (i.e., chances for performance-dependent reward) and dangers (i.e., performance-dependent risks of loss). Attentional biases towards opportunity and danger signals should therefore be functionally valuable. By contrast, the functional value of attentional biases towards signals of performance-independent (i.e., uncontrollable) rewards or losses is not obvious. The present study compares attentional biases towards positive and negative stimuli, depending on whether the stimuli signal performance-dependent or performance-independent reward or loss. Specifically, we induced colour-valence associations before engaging participants in an additional-singleton task that measures attentional bias. In the valence-induction phase, one colour signalled a potential reward, and another colour signalled a potential loss; importantly, in one group, rewards and losses were performance-dependent, whereas in another group, they were performance-independent (i.e., seemingly random). In the subsequent additional-singleton task, we found increased additional-singleton effects for colours associated with performance-dependent rewards and losses (i.e., opportunities and dangers). If, however, rewards and losses were performance-independent, the singleton effect was enhanced only for reward but not loss stimuli.

Clinical validation of a liquid chromatography-tandem mass spectrometry method for the quantification of calcineurin and mTOR inhibitors in dried matrix on paper discs

IntroductionAdvances in liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS) have enabled the quantification of immunosuppressants using microsampling techniques. In this context, dried matrix on paper discs (DMPD) could be a useful alternative to conventional venipuncture. Although analytical validation is necessary to establish the suitability of method performance, it is not sufficient to proceed with its implementation into routine clinical practice. Also necessary is that equivalence between sampling methods be demonstrated in a clinical validation study. ObjetivesTo clinically validate a LC-MS/MS method for the quantification of tacrolimus, sirolimus, everolimus and cyclosporin A using DMPD. MethodsAccording to the recommendations of international guidelines, at least 40 whole blood (WB) and DMPD paired samples for each analyte were collected by skilled technicians and analyzed using LC-MS/MS. Results were evaluated in terms of statistical agreement and bias values at medical decision points. ResultsFor all analytes, Passing-Bablok regression analysis revealed that confidence intervals (CIs) for slopes and intercepts included 1 and 0, respectively. It also showed that biases at medical decision points were not clinically relevant. No statistically significant differences between DMPD and WB were found using difference plots and agreement analysis. In this regard, CIs for bias estimators included 0, and more than 95% of the results fell within the limits of agreement. ConclusionThe feasibility of the clinical application of simultaneous quantification of tacrolimus, sirolimus, everolimus and cyclosporin A in DMPD was demonstrated. Results showed that this microsampling technique is interchangeable with conventional WB sampling when specimens are collected by trained personnel.

Improved Field‐Aligned Current and Radial Current Estimates at Low and Middle Latitudes Deduced by the Swarm Dual‐Spacecraft

AbstractIonospheric currents have widely been investigated by using magnetic measurements from low‐Earth orbiting satellites. However, the assumptions of deriving currents from the magnetic measurements have not always been well considered. In this study we performed a detailed analysis of the ionospheric radial current (IRC) and inter‐hemispheric field‐aligned current (IHFAC) estimates at equatorial and low latitudes derived from the single‐satellite and dual‐spacecraft (dual‐SC) approaches of European Space Agency (ESA's) Swarm constellation. Data considered cover a 5‐year period, from 17 April 2014 to 16 April 2019. We found for most of the cases, the IRCs and IHFACs derived from both approaches show consistent latitudinal profiles. However, there are several cases with discrepancy exceeding 5 nA/m2 between two approaches. On average, the diurnal variations of IHFACs from both approaches agree well with each other for all seasons. But the amplitudes of single‐satellite results reach only about 70% of those from the dual‐SC. This difference is attributed to the fact that only the magnetic field By component is utilized in the single‐satellite approach, while both Bx and By components are considered in the dual‐SC approach. Above the magnetic equator, the IRCs derived from single‐satellite approach show clear tidal signatures, while such signature cannot be found in the IRCs from dual‐SC approach. We interpret these tidal‐signature of IRCs as spurious results, caused by equatorial electrojet contributions to the ∆By component. The dual‐SC derived IRCs show notable differences between ascending and descending orbits. Such differences might be due to a violation of the assumed perfect calibration of Swarm A and C. We suggest a systematic spacecraft‐fixed bias in the along‐track magnetic field component (Bx) between Swarm A and C. By interpreting the IRC differences, we obtain bias values of ∆Bx reaching 1 nT. Our results reveal that ionospheric currents are better characterized by the dual‐SC approach. But comparison with single‐satellite current estimates can help to identify weakness.

Thermal hysteresis of magnetization in NiFe/IrMn exchange-biased ferromagnet

Magnetization reversal in NiFe/IrMn exchange-biased thin films was investigated under thermal cycling in an external magnetic field, applied opposite to the direction of the exchange bias field. Thermal hysteresis of magnetization accompanied by changes in magnetization polarity was observed in the applied field close to the exchange bias value. This effect appears when thermally induced variations of the exchange bias exceed the corresponding variations in coercivity. The amplitude of magnetization reversal in NiFe/IrMn structures exceeds ∼100 times the corresponding amplitude in spin-crossover molecular compounds. The observed bistability of the magnetic state, revealed by thermal hysteresis, gradually disappears with an increase in the number of cooling–heating thermal cycles, that indicates an irreversible quenching of the interfacial magnetization configuration. This effect paves the way for the creation of a new class of switching devices with thermally assisted bistability in the ferromagnetic state.

Accuracy of photoplethysmography-based measurement of blood pressure

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Photoplethysmography (PPG)-based blood pressure (BP) measurements are available in the latest smartwatches and are thus available for patients in the consumer market. While the devices typically comply with the medical device regulation, accuracy is often not known and data on validation are not publicly available. Purpose To investigate the accuracy and precision of the Samsung Galaxy Watch Active 2 PPG-based BP measurement as compared to conventional BP measurement and to investigate clinical applicability. Methods 31 patients scheduled for ambulatory 24-hour BP measurement were included in the study. 24-hour BP devices were attached to the non-dominant arm, smartwatches were attached to the opposite arm. Calibration was performed as instructed by the manufacturer. The smartwatch was used to register BP intermittently for 24 hours. Results from both devices were compared using the Taffé method. Results and discussion Mean age was 59 years. 17 patients (55%) were male. Comparison of the two methods is demonstrated in Figure 1. The smartwatch overestimates the systolic BP up to approximately 132 mm Hg and then underestimates the systolic BP, illustrating the presence of proportional (0.48) and differential (69.0) bias. Precision for the gold standard method was higher compared to the smartwatch measurements. For increasing values of systolic BP, the precision of the smartwatch decreases. For diastolic BP, differential (31.4) and proportional bias (0.68) are present, resulting in an overestimation of the diastolic BP at lower values (<100 mmHg). Precision for the gold standard method was higher at lower BP values, while precision for smartwatch measurements was higher at higher BP values (i.e. there was a lower standard deviation of measurement error). Figure 1 Bias and precision plots for systolic and diastolic blood pressure (BP). In each figure the x-axis indicates the best linear unbiased prediction (BLUP) or true value of BP. Panel A and B indicate the bias plots. Smartwatch measurements (blue dashed lines) are compared to reference measurements (solid black line); both values are read from the leftmost y-axis. The bias (difference of smartwatch measurement compared to the reference method) is depicted as a solid red line; the value of bias is read from the rightmost y-axis. Panel C and D indicate the precision plots, indicating the standard deviation of measurement errors for both measurement methods. The reference method is depicted in black; the smartwatch method is depicted in blue. Conclusion There is a differential and proportional bias in PPG-based BP measurement of systolic and diastolic BP measurement. The systolic BP is overestimated at lower values and underestimated at higher values. This might indicate potential for application as a screening tool for hypertension. Further research is needed to investigate clinical applicability.

A 6-ring embedded strain sensor

The proposed embedded strain sensor consists of six elastic rings, placed in an icosahedral symmetry, whose measured elongations give access to the complete 3D strain tensor in the surrounding matrix. The linear relationship between the ring elongations and the strain tensor in the matrix is given. From this relation it is deduced that the tensor is isotropic, i.e. its sensitivity is independent of the rotation of the strain tensor and that the sensor is more sensitive to hydrostatic strains than to deviatoric strains. The additional perturbation of the strain field in the matrix, induced by the presence of the sensor, is studied by means of a numerical integration of the Kelvin solution and the use two dimensionless factors relating to geometry and elasticity. From this study we obtain, in a generic way, an evaluation of the intensity of this additional strain field and the value of the measurement bias due to the elasticity of the ring, which allows in practice to reduce this bias. A draft of the realisation is proposed, for which it is verified that the disturbance of the strain field in the matrix as well as the bias to be corrected are small.