Recalibration Process Research Articles

Control and recalibration of path integration in place cells using optic flow.

Hippocampal place cells are influenced by both self-motion (idiothetic) signals and external sensory landmarks as an animal navigates its environment. To continuously update a position signal on an internal 'cognitive map', the hippocampal system integrates self-motion signals over time, a process that relies on a finely calibrated path integration gain that relates movement in physical space to movement on the cognitive map. It is unclear whether idiothetic cues alone, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if polarizing position information provided by landmarks is essential for this recalibration. Here, we demonstrate both recalibration of path integration gain and systematic control of place fields by pure optic flow information in freely moving rats. These findings demonstrate that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks.

Towards optimal model evaluation: enhancing active testing with actively improved estimators

With rapid advancements in machine learning and statistical models, ensuring the reliability of these models through accurate evaluation has become imperative. Traditional evaluation methods often rely on fully labeled test data, a requirement that is becoming increasingly impractical due to the growing size of datasets. In this work, we address this issue by extending existing work on active testing (AT) methods which are designed to sequentially sample and label data for evaluating pre-trained models. We propose two novel estimators: the Actively Improved Levelled Unbiased Risk (AILUR) and the Actively Improved Inverse Probability Weighting (AIIPW) estimators which are derived from nonparametric smoothing estimation. In addition, a model recalibration process is designed for the AIIPW estimator to optimize the sampling probability within the AT framework. We evaluate the proposed estimators on four real-world datasets and demonstrate that they consistently outperform existing AT methods. Our study also shows that the proposed methods are robust to changes in subsample sizes, and effective at reducing labeling costs.

Concrete Pavement Design Analysis Using AASHTOWare Pavement Mechanistic-Empirical Design Software

Local calibration of pavement performance models embedded in the AASHTOWare Pavement Mechanistic-Empirical Design (PMED) software may be needed to implement this design methodology. A recent survey showed that, despite local calibration, several state highway agencies in the U.S. are using the American Association of State Highway Transportation Officials (AASHTO) Guide for Design of Pavement Structures (1993 version) and the PMED software for comparative design of jointed plain concrete pavement (JPCP). This paper describes the recalibration process of the JPCP distress models in the PMED software and comparative designs obtained by the PMED software and 1993 AASHTO design guide. The results show that the calibration/recalibration process depends on the availability of measured performance data. Not all global coefficients must be changed in the calibration/recalibration process. The 1993 AASHTO design guide yielded higher JPCP slab thickness for projects with high truck traffic than the PMED software when no friction is assumed between the slab and a cementitious base. An assumption of full friction will have a discernible effect on the slab thickness obtained from PMED.

CALIBRATION OF THE INERTIAL MEASUREMENT UNIT IN CRITICAL CONDITIONS

Calibration of the inertial measuring unit (IMU) is an independent task and is the most important stage in preparing for operation both platform and strapdown inertial navigation systems (INS). The traditional calibration of IMU, containing at least three gyroscopes and three accelerometers, carried out in specialized testing laboratories, has been studied in sufficient detail. However, the widespread use of IMUs using MEMS sensors with characteristic instability of parameters leads to the need for additional calibration of IMUs IMUediately before INS operation in critical or field conditions. Under such conditions, firstly, there is no testing equipment necessary for the full implementation of traditional calibration, and secondly, the recalibration process itself is limited in time.In the article, studies were carried out on the possibility of using the method of successive rotations for the recalibration of IMU in the field. With this approach, in contrast to the test rotation method, there is no need to rotate the IMU by 180 degrees, but it is enough to rotate the IMU by small angles. Calculation ratios are obtained for determining the scale factors and zero signals of the accelerometer unit for two positions of the IMU. The experiment carried out confirmed the validity of the calculated ratios obtained. In addition, a recalibration method is described, which makes it possible to estimate, in addition to scale factors and zero signals of the accelerometer unit, also the tilt angles of the base on which the IMU is installed. To do this, you must have at least five different IMU positions.In addition, the calibration of the gyroscope unit when tactical and navigational accuracy class gyroscopes are used is considered. For gyroscopes of the tactical accuracy class and which are manufactured using MEMS technology, biases are determined in the critical conditions, which are not stable from start to start. Algorithms for determining biases and scale factors were obtained for the calibration of the gyroscope unit of the navigation accuracy class, for which the same two positions in which the accelerometer unit was calibrated were used. That is, during the calibration of the IMU in critical conditions, it is possible to simultaneously calibrate the gyroscope unit and the accelerometer unit, which saves the overall calibration time.

The effect of COVID-19 in estimating the South African real spot-rate curve

This study investigates the dynamism of different mathematical term-structure models during COVID-19 to estimate the South African real spot-rate curve. The study is based on term-structure models following Nelson-Siegel framework; and further incorporates the recalibration process on static term-structure models over different economic environments. It was found that the recalibration methodology resulted in no improvements on Linear-parametric and Cubic-splines term-structure models. However, the recalibration effect on Nelson-Siegel and Svensson models resulted in an improved fit for South African inflation-indexed spot rates even in periods of heightened market risk.

Unveiling a new era with liquid chromatography coupled with mass spectrometry to enhance parathyroid hormone measurement in patients with chronic kidney disease

Precise determination of circulating parathyroid hormone (PTH) concentration is crucial to diagnose and manage various disease conditions, including the chronic kidney diseases-mineral and bone disorder. However, the lack of standardization in PTH assays is challenging for clinicians, potentially leading to medical errors because the different assays do not provide equivalent results and use different reference ranges. Here, we aimed to evaluate the impact of recalibrating PTH immunoassays by means of a recently developed LC-MS/MS method as the reference. Utilizing a large panel of pooled plasma samples with PTH concentrations determined by the LC-MS/MS method calibrated with the World Health Organization (WHO) 95/646 International Standard, five PTH immunoassays were recalibrated. The robustness of this standardization was evaluated over time using different sets of samples. The recalibration successfully reduced inter-assay variability with harmonization of PTH measurements across different assays. By recalibrating the assays based on the WHO 95/646 International Standard, we demonstrated the feasibility for standardizing PTH measurement results and adopting common reference ranges for PTH assays, facilitating a more consistent interpretation of PTH values. The recalibration process aligns PTH results obtained from various immunoassays with the LC-MS/MS method, providing more consistent and reliable measurements. Thus, establishing true standardization across all PTH assays is crucial to ensure consistent interpretation and clinical decision-making.

Forecasting the South African Real Spot-rate Curve During Covid-19 Stress

Purpose: The aim of this study is to investigate the performance of forecasting the South African real spot rate curve using different mathematical term-structure models during the Covid-19 period. This study follows previous studies by Reid (2009) and Mashoene et al., 2021) where dynamic term-structure models performed well compared to static term-structure models over normal market conditions. For high volatility, normality often breaks, which might result in a need for recalibration in the currently used methodology or a total change in the methodology.
 Methodology: This study explores the dynamism of both dynamic term-structure models, which follow the Nelson-Siegel framework, and static term-structure models with the option of recalibration of model parameters to account for a change in macro-economic dynamics brought by the effect of the Covid-19 pandemic. The forecasting exercise was applied to term-structure models following the Nelson-Siegel framework using AR(1) process.
 Findings: The incorporation of macroeconomic factors performed poorly in estimating future model parameters. This could be attributable to the effect of global stress, which resulted in poor linear relationships between macroeconomic and financial factors. Recalibration process on static Nelson-Siegel term-structure models improved the models' performance, especially during the high volatility/period of total economic shutdown due to the Covid-19 pandemic.
 Implications: The application of static Nelson-Siegel term-structure models with recalibrated parameters might improve bond pricing processes and risk management strategies in emerging markets, especially during market stress.

Comparison of several strategies for the deployment of a multivariate regression model on several handheld NIR instruments. Application to the quality control of medicines

Chemometrics applied to spectroscopic measurements such as near-infrared are gaining more and more importance for quality control of pharmaceutical products. Handheld near-infrared devices show great promise as a medicines quality screening technique for post-marketing surveillance. These devices are able to detect substandard and falsified medicines in pharmaceutical supply chains and enable rapid action before these medicines reach patients. The instrumental and environmental changes, expected or not, can adversely affect the analytical performances of prediction models developed for routine applications. Based on a previous study, PLS prediction models were developed and validated on three similar handheld NIR transmission spectrophotometers of the same model and from same company. These models have shown to be effective in analyzing metformin tablet samples, but significant spectral differences between handheld systems complicated their deployment for routine analysis. In this study, different strategies have been applied and compared to correct the instrumental variations, including global modelling (GM) and calibration transfer methods (Direct Standardization, DS; Spectral Space Transformation, SST and Slope/Bias correction, SBC), considering the RMSEP and the accuracy profile as assessment criteria. The transfer methods showed good capabilities to maintain the predictive performances comparable to that of the global modelling approach, except for a remaining slight bias. This approach is interesting since very few standardization samples are required to develop an adequate transfer model. GM, SST and SBC were able to correct/handle drifts in the spectral responses of different handheld instruments and thus may help to avoid the need for a long, laborious, and costly full recalibration process due to inter-instrument variations.

Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings

Aims/hypothesisWe aimed to compare the performance of risk prediction scores for CVD (i.e., coronary heart disease and stroke), and a broader definition of CVD including atrial fibrillation and heart failure (CVD+), in individuals with type 2 diabetes.MethodsScores were identified through a literature review and were included irrespective of the type of predicted cardiovascular outcome or the inclusion of individuals with type 2 diabetes. Performance was assessed in a contemporary, representative sample of 168,871 UK-based individuals with type 2 diabetes (age ≥18 years without pre-existing CVD+). Missing observations were addressed using multiple imputation.ResultsWe evaluated 22 scores: 13 derived in the general population and nine in individuals with type 2 diabetes. The Systemic Coronary Risk Evaluation (SCORE) CVD rule derived in the general population performed best for both CVD (C statistic 0.67 [95% CI 0.67, 0.67]) and CVD+ (C statistic 0.69 [95% CI 0.69, 0.70]). The C statistic of the remaining scores ranged from 0.62 to 0.67 for CVD, and from 0.64 to 0.69 for CVD+. Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95% CI 0.37, 0.39) to 0.74 (95% CI 0.72, 0.76) for CVD, and from 0.41 (95% CI 0.40, 0.42) to 0.88 (95% CI 0.86, 0.90) for CVD+. A simple recalibration process considerably improved the performance of the scores, with calibration slopes now ranging between 0.96 and 1.04 for CVD. Scores with more predictors did not outperform scores with fewer predictors: for CVD+, QRISK3 (19 variables) had a C statistic of 0.68 (95% CI 0.68, 0.69), compared with SCORE CVD (six variables) which had a C statistic of 0.69 (95% CI 0.69, 0.70). Scores specific to individuals with diabetes did not discriminate better than scores derived in the general population: the UK Prospective Diabetes Study (UKPDS) scores performed significantly worse than SCORE CVD (p value <0.001).Conclusions/interpretationCVD risk prediction scores could not accurately identify individuals with type 2 diabetes who experienced a CVD event in the 10 years of follow-up. All 22 evaluated models had a comparable and modest discriminative ability.Graphical abstract

Flexible use of post-saccadic visual feedback in oculomotor learning.

Saccadic eye movements bring objects of interest onto our fovea. These gaze shifts are essential for visual perception of our environment and the interaction with the objects within it. They precede our actions and are thus modulated by current goals. It is assumed that saccadic adaptation, a recalibration process that restores saccade accuracy in case of error, is mainly based on an implicit comparison of expected and actual post-saccadic position of the target on the retina. However, there is increasing evidence that task demands modulate saccade adaptation and that errors in task performance may be sufficient to induce changes to saccade amplitude. We investigated if human participants are able to flexibly use different information sources within the post-saccadic visual feedback in task-dependent fashion. Using intra-saccadic manipulation of the visual input, participants were either presented with congruent post-saccadic information, indicating the saccade target unambiguously, or incongruent post-saccadic information, creating conflict between two possible target objects. Using different task instructions, we found that participants were able to modify their saccade behavior such that they achieved the goal of the task. They succeeded in decreasing saccade gain or maintaining it, depending on what was necessary for the task, irrespective of whether the post-saccadic feedback was congruent or incongruent. It appears that action intentions prime task-relevant feature dimensions and thereby facilitated the selection of the relevant information within the post-saccadic image. Thus, participants use post-saccadic feedback flexibly, depending on their intentions and pending actions.

INDUCTANCE VALIDATION OF LCR METER AFTER RECALIBRATION

Laboratory of National Measurement Standard for Electricity and Time (NMS ET Lab) has validated the measurement results pre and post the LCR meter recalibration process on inductance parameters. The validation process was carried out at nominal values of 100 μH, 1 mH, 10 mH, 100 mH and 1 H in accordance to the reference standards. The direct measurement method was used to measure the inductance parameter of LCR meter with standard inductor. Validation is carried out by comparing the measurement results pre and post the LCR meter recalibration process at SCL - Hong Kong. Based on the E n number analysis, the values between -0.21 to 0.44 were obtained for 5 measurement points, therefore the calibration system and method used by NMS ET Lab are valid and have measurement results that are equivalent to other countries.

Scale attentive network for scene recognition

Scene recognition aims at classifying a scene image to one of the predefined scene categories by comprehending the entire image. The complex composition of scenery images makes scene recognition a challenging task. However, most state-of-the-art visual recognition methods are developed on general-purpose datasets and omit the uniqueness of scene data. In this work, we propose an efficient Scale Attentive (SA) Module to address the predicament of scene recognition, which streamlines the scale-aware attention learning pipeline to assist the feature re-calibration and refinement process. By integrating SA Module into ResNet-50, we obtain a boost of Top-1 accuracy by 1.83% on the benchmark scene dataset with only 0.12% additional parameters and 0.24% additional FLOPs. Moreover, comprehensive experiments show that our method achieves better performance compared with the state-of-the-art attention and multi-scale methods in a computationally efficient manner.

Development of NIRS re-calibration model for ethiopian barley (Hordeum vulgare) lines traits to determine their brewing potential

Barley is one of the top five grain crops in Ethiopia, covering approximately 1 million hectares, and is grown for food, animal's feed and industrial purposes. Ethiopian Agricultural Research Institute (EIAR) is also conducting a barley research and breeding program with the aim of improving grain yield and resilience through screening lines. Sample materials used for the experiment were taken from 2018, 2019 and 2020 seasons to capture the variation in the growing year. As a result, the spectral data of the samples and the wet chemical data were generated using official international methods. Subsequently, a recalibration based on 280 samples of breeding lines of different genotypes, growing years and locations was developed in order to predict the protein, extract and friability content characteristics of important barley malt parameters. Wet chemical data were collected from processed/malted barley grains. A calibration model was created on the basis of the calibration software OPUS LAB 7.5 from Bruker Optics GmbH. The protein calibration (R 2 c = 0.90; RPD = 3.8) can be viewed as broadly applicable; the extract content (R 2 c = 0.88 and RPD = 2.8) and friability (R 2 c = 0.85 and RPD = 2.7) could be useful as well as a good predictive model for line screening. The calibration model was missing to predict long-range sample sources before the model range was expanded by recalibration process including different sample sources. Therefore, these NIRS models enable the selection of suitable food and malt barley genotypes at wide range. Since NIRS is fast and inexpensive, the barley improvement program can increase selection intensity, especially in the early testing phase. So the best precision of the NIRS recalibration discovered in this study enables to identify top candidate lines with a minimum of errors. • The aim was predicting grain barley quality trait using near infrared spectroscopy. • The 2018, 2019 and 2020 seasons of 280 barley genotypes were used for the study. • Models developed for protein, extract & friability from reference ^ spectral data. • Protein, extract & friability model predictive result is 3.8, 2.8 and 2.7 respectively. • A predictive model (>2.5) & (2–2.5) are excellent and good respectively.

Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care setting

Abstract People with type 2 diabetes (T2DM) remain at high risk for cardiovascular disease (CVD) CVD treatment initiation and intensification are guided by risk prediction algorithms. The majority of CVD risk prediction tools have not been validated in T2DM. We compared the performance of general and diabetes specific cardiovascular risk prediction scores for cardiovascular disease (CVD ie coronary heart disease and stroke), CVD+ (including atrial fibrillation and heart failure), and their individual components, in type 2 diabetes patients (T2DM). Scores were identified through a systematic review and included irrespective of the type of predicted CVD, or inclusion of T2DM patients. Performance was assessed in a contemporary sample of 203,172 UK T2DM. We identified 22 scores: 11 derived in the general population, 9 in T2DM patients, and 2 excluded T2DM patients. Over 10 years follow-up, 63,000 events occurred. The RECODE score, derived in people with T2DM, performed best for both CVD (c-statistic 0.731 (0.728,0.734), and CVD+ (0.732 (0.729,0.735)). Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95% CI 0.37; 0.39) to 1.05 (95% CI 1.03; 1.07). A simple, population specific recalibration process considerably improved performance, now ranging between 0.98 and 1.03. Risk scores performed badly in people with pre-existing CVD (c-statistic ∼0.55). CVD risk prediction scores performed worse in T2DM than in the general population, irrespective of derivation population, and of original predicted outcome. Scores performed especially poorly in patients with established CVD. A simple population specific recalibration markedly improved score performance and is recommended for future use. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): NPIF programme

Calibration of scatterometer wind speed under hurricane conditions

AbstractFour scatterometers, namely: METOP-A, METOP-B, ERS-2 and OCEANSAT-2 were re-calibrated against combined National Data Buoy Center (NDBC) data and aircraft Stepped Frequency Microwave Radiometer (SFMR) data from hurricanes. As a result, continuous calibration relations over the wind speed range 0 to 45 ms-1 were developed. The calibration process uses match-up criteria of 50 km and 30 min for the buoy data. However, due to the strong spatio-temporal wind speed gradients in hurricanes, a method which considers both scatterometer and SFMR data in a storm-centred translating frame of reference is adopted. The results show that although the scatterometer radar cross-section is degraded at high wind speeds, it is still possible to recover wind speed data using the re-calibration process. Data validation between the scatterometers shows that the calibration relations produce consistent results across all scatterometers and reduce the bias and root mean square error compared to previous calibrations. In addition, the results extend the useful range of scatterometer measurements to as high as 45 ms-1.

PO-1783: A method for performing film dosimetry as part of a postal audit service by a recalibration process

PO-1783: A method for performing film dosimetry as part of a postal audit service by a recalibration process

Role of the Orbitofrontal Cortex in the Computation of Relationship Value

ABSTRACT This research investigated whether the medial orbitofrontal cortex (OFC), which is known to code the value of various rewards, is involved in the relationship value recalibration process. Previous research suggests that people upregulate the relationship value of a specific friend in response to the friend’s commitment signals. In a functional magnetic resonance imaging study (Study 1), participants imagined receiving high-cost commitment signals, low-cost commitment signals, or no signals from a particular friend. Participants’ subjective rating of the relationship value upregulation was positively correlated with medial OFC activity. Subtraction analyses showed that high-cost commitment signals engaged the medial OFC more than did signal failures. An auxiliary analysis revealed that medial OFC activity in response to low-cost commitment signals was negatively correlated with loneliness. To follow-up these findings, we conducted an online vignette study (Study 2), in which participants rated the relationship value of a real friend before and after imagining receiving a series of low-cost commitment signals from that friend. Corroborating the upregulation hypothesis, perceived relationship value significantly increased after imagining a series of commitment signals. This effect was weaker among individuals high in loneliness.

Rapid Temporal Recalibration to Audiovisual Asynchrony Occurs Across the Difference in Neural Processing Speed Based on Spatial Frequency.

Audiovisual integration relies on temporal synchrony between visual and auditory stimuli. The brain rapidly adapts to audiovisual asynchronous events by shifting the timing of subjective synchrony in the direction of the leading modality of the most recent event, a process called rapid temporal recalibration. This phenomenon is the flexible function of audiovisual synchrony perception. Previous studies found that neural processing speed based on spatial frequency (SF) affects the timing of subjective synchrony. This study examined the effects of SF on the rapid temporal recalibration process by discriminating whether the presentation of the visual and auditory stimuli was simultaneous. I compared the magnitudes of the recalibration effect between low and high SF visual stimuli using two techniques. First, I randomly presented each SF accompanied by a tone during one session, then in a second experiment, only a single SF was paired with the tone throughout the one session. The results indicated that rapid recalibration occurred regardless of difference in presented SF between preceding and test trials. The recalibration magnitude did not significantly differ between the SF conditions. These findings confirm that intersensory temporal process is important to produce rapid recalibration and suggest that rapid recalibration can be induced by the simultaneity judgment criterion changes attributed to the low-level temporal information of audiovisual events.

Calibration transfer across multiple hyperspectral imaging-based plant phenotyping systems: I – Spectral space adjustment

Hyperspectral Imaging is one of the most popular technologies in plant phenotyping. Various kinds of hyperspectral imaging systems have been developed in the past years. However, there are always significant differences in sensors and imaging environmental conditions between different facilities, which makes it difficult to share image processing algorithms and plant features prediction models. Calibration transfer between the imaging systems is critically important. The white referencing calibration has been proved effective in removing the major lighting intensity differences, but significant spectral differences between systems still exist. In this study, we considered the calibration transfer as a spectral adjustment process and compared four different methods (DS, PDS, DPDS and SST) for adjusting the spectra. To perform the calibration transfer, maize plants were imaged using push-broom style, top-view VNIR hyperspectral camera in two greenhouse phenotyping facilities (inter-facility) and within the same facility (intra-facility) under different imaging conditions. The suggested spectral adjustment methods were tested using master partial least squares regression (PLSR) based calibration models developed for predicting the relative water content (RWC) and nitrogen content (N) of maize plants. Results showed that spectral space transformation (SST) decreased the RMSEv from 9.450% and 10.636% to 3.280% and 2.424% for the predicted RWC of intra and inter-facility transfer, respectively, while the corresponding measures achieved by direct standardization (DS) were 3.412% and 3.105%, respectively. In case of N predictions, similar results were observed for DS and SST. The other two methods (PDS and DPDS) reduced the prediction error for RWC and N, but their performance was not on par with DS and SST. The results indicated that the proposed methods, especially DS and SST were able to alleviate the perturbations inherited in the spectra and thus can help to avoid the need for time-consuming, labor-intensive and costly full recalibration process that arose due to the intra or inter-facility variations.

Optimizing the recalibration process in radiochromic film dosimetry

Intra-lot, inter-scan and other variabilities in radiochromic film dosimetry may have a severe impact on absolute dosimetry with this dosimeter. In the literature, several dosimetry protocols may be found characterized by different calibration functions and different film response variables. Also, the re-calibration methods found in the literature correct and minimize the impact of the variabilities in the absolute dose estimates. In this work, several recalibration methods and dosimetry protocols are evaluated. In order to find optimal configurations, their accuracy is compared, and the accuracy level that can be reached in each case is discussed. The efficient protocol and the parameter escalation are used to recalibrate EBT3 films from two different film batches. The mean absolute deviations between known doses and estimated doses for eight dose levels are obtained and compared with the self calibration of each reading, named intrinsic film calibration. Eight film sheets from two different lots and two digitizers are used. The parameter escalation method with a four-level recalibration using net optical density (NOD) and a power law as dosimetry protocol obtains the highest accuracy. Regarding the number of control strips, increasing the number from two to three makes the parameter escalation protocol to come close to intrinsic film calibration in all cases, but has a less important effect on the efficient protocol. Regardless the choice of the sensitometric variables, using the appropriate recalibration method results in accuracy levels typical of self calibration of the film. In addition, the parameter escalation method provides better results than the efficient protocol with three calibration strips.