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Modern functionals based on the exact-exchange (EXX) energy density like local hybrid functionals (LHs) or range-separated LHs have recently received additional attention due to their advantages over established functionals when it comes to the local balance between self-interaction errors and static-correlation errors. A possible theoretical drawback of such functionals over the years has been the so-called gauge problem due to the inherent ambiguity of exchange-energy densities. Modern LHs like LH20t or more sophisticated functionals based thereon have been constructed using suitably optimized calibration functions (CFs) to minimize the mismatch of the semi-local and EXX energy densities. Here, we show that the unphysical contributions arising from the gauge problem may also be reduced significantly without a CF by tailoring the position-dependence of the EXX admixture (local mixing function, LMF) in a way to suppress spurious positive energy-density contributions locally in space. This is achieved by building the so-called x-LMFs upon the ratio between EXX and semi-local exchange-energy densities. The resulting LH24x functional provides similar accuracy, e.g., for the GMTKN55 test suite, as LH20t, but without introduction of a CF! We provide detailed comparative analyses of integrated energies and spatially resolved energy densities. The good performances of LHs for chemically relevant energy differences are to some extent due to the core nature of unphysical artifacts that cancel out efficiently. [ABSTRACT FROM AUTHOR]

We present a comprehensive guide to light-sheet microscopy (LSM) to assist scientists in navigating the practical implementation of this microscopy technique. Emphasizing the applicability of LSM to image both static microscale and nanoscale features, as well as diffusion dynamics, we present the fundamental concepts of microscopy, progressing through beam profile considerations, to image reconstruction. We outline key practical decisions in constructing a home-built system and provide insight into the alignment and calibration processes. We briefly discuss the conditions necessary for constructing a continuous 3D image and introduce our home-built code for data analysis. By providing this guide, we aim to alleviate the challenges associated with designing and constructing LSM systems and offer scientists new to LSM a valuable resource in navigating this complex field. [ABSTRACT FROM AUTHOR]

Semiconductor quantum dots are favorable candidates for quantum information processing due to their long coherence time and potential scalability. However, the calibration and characterization of interconnected quantum dot arrays have proven to be challenging tasks. One method to characterize the configuration of such an array involves using the Hubbard model. In this paper, we present an efficient characterization algorithm that efficiently extracts the Hubbard model parameters, including tunnel coupling and capacitive coupling energy, from experimental stability diagrams. Leveraging the dual-annealing optimizer, we determine the set of Hubbard parameters that best characterize the experimental data. We compare our method with an alternate, well-established measure of tunnel coupling and find good agreement within the investigated regime. Our extracted tunnel couplings range from 69 to 517 μ eV, and we discuss the limiting factors of our method. [ABSTRACT FROM AUTHOR]

PURPOSE: CT Hounsfield Units (HUs) are converted to electron density using a calibration curve obtained from physical measurements of an electron density phantom. HU values assigned to an MRI-derived synthetic computed tomography (sCT) may present a different relationship with electron density compared to CT HU. Correct assignment of sCT HU values is critical for accurate dose calculation and delivery. The goals of this work were to develop a sCT calibration curve using patient data acquired on a clinically commissioned CT scanner and assess for CyberKnife- and volumetric modulated arc therapy (VMAT)-based MR-only treatment planning of prostate SBRT. METHODS: Same-day CT and MRI simulation in the treatment position were performed on 10 patients treated with SBRT to the prostate. Dixon in-phase and out-of-phase MRIs were acquired on a 3T scanner using a 3D T1-weighted gradient-echo sequence to generate sCTs using a commercial sCT algorithm. CT and sCT datasets were co-registered and HU values compared using mean absolute error (MAE). An optimized HU-to-density calibration curve was created based on average HU values across an institutional patient database for each of the four sCT tissue types. Clinical CyberKnife and VMAT treatment plans were generated on each patient CT and recomputed onto corresponding sCTs. Dose distributions computed using CT and sCT were compared using gamma criteria and dose-volume-histograms. RESULTS: For the optimized calibration curve, HU values were -96, 37, 204, and 1170 and relative electron densities were 0.95, 1.04, 1.1, and 1.7 for adipose, soft tissue, inner bone, and outer bone, respectively. The proposed sCT protocol produced total MAE of 94 ± 20HU. Gamma values mean ± std (min-max) were 98.9% ± 0.9% (97.1%-100%) and 97.7% ± 1.3% (95.3%-99.3%) for VMAT and CyberKnife plans, respectively. CONCLUSION: MRI-derived sCT using the proposed approach shows excellent dosimetric agreement with conventional CT simulation, demonstrating the feasibility of MRI-derived sCT for prostate SBRT treatment planning.

While computational screening with density functional theory (DFT) is frequently employed for the screening of metal–organic frameworks (MOFs) for gas separation and storage, commonly applied generalized gradient approximations (GGAs) exhibit self-interaction errors, which hinder the predictions of adsorption energies. We investigate the Hubbard U parameter to augment DFT calculations for full periodic MOFs, targeting a more precise modeling of gas molecule–MOF interactions, specifically for N2, CO2, and O2. We introduce a calibration scheme for the U parameter, which is tailored for each MOF, by leveraging higher-level calculations on the secondary building unit (SBU) of the MOF. When applied to the full periodic MOF, the U parameter calibrated against hybrid HSE06 calculations of SBUs successfully reproduces hybrid-quality calculations of the adsorption energy of the periodic MOF. The mean absolute deviation of adsorption energies reduces from 0.13 eV for a standard GGA treatment to 0.06 eV with the calibrated U, demonstrating the utility of the calibration procedure when applied to the full MOF structure. Furthermore, attempting to use coupled cluster singles and doubles with perturbative triples calculations of isolated SBUs for this calibration procedure shows varying degrees of success in predicting the experimental heat of adsorption. It improves accuracy for N2 adsorption for cases of overbinding, whereas its impact on CO2 is minimal, and ambiguities in spin state assignment hinder consistent improvements of O2 adsorption. Our findings emphasize the limitations of cluster models and advocate the use of full periodic MOF systems with a calibrated U parameter, providing a more comprehensive understanding of gas adsorption in MOFs. [ABSTRACT FROM AUTHOR]

The field of multifunctional energetics encompasses a range of materials including propellants, explosives, and pyrotechnics that possess the ability to be manipulated through various characteristics that can be switched between go and no-go, or those that have controllable energy release levels or have additional functions beyond energetic output. The development of multifunctional energetics harnessing electromechanical or piezoelectric properties of polymeric materials or binder systems has garnered increasing interest in recent years. Among polymers, fluoropolymers such as poly(vinylidene fluoride) (PVDF) and copolymers such as poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)], which are used as the binder and oxidizer in the energetic formulations, have demonstrated the highest piezoelectric coefficients. In this study, we fabricated piezo-energetic composite films using aluminum nanopowders (10 wt. % active content) as fuel and P(VDF-TrFE) (70/30) as an oxidizer and investigated the piezoelectric response using a small-scale drop weight setup. Additionally, we employed a shaker setup to investigate the response of the films to vibrations. Our findings demonstrate that these piezo-energetic films can replicate the behavior of a commercial PVDF gauge at relatively low-pressure impacts, indicating their potential use as shock or pressure sensors in various fields, as well as an accelerometer gauge. Additionally, aging studies of up to one year indicated minimal loss in the energetic content of the created films, enabling the use of energetic gauges for an extended period. Our findings support the effectiveness of piezo-energetic composite films as pressure sensors or accelerometers and highlight their potential for energetic applications. [ABSTRACT FROM AUTHOR]

Despite more than seven decades of active research and development in thermoelectricity, the accurate measurement of the thermoelectric (TE) properties of bulk materials has remained a challenge, mainly because of the strong interrelation between thermal and electrical phenomena. This work highlights practical advancements in methods and instrumentation dedicated to the simultaneous measurements of TE properties such as the Seebeck coefficient (S), the thermal (κ), and electrical (σ) conductivities and the dimensionless TE figure of merit ZT = S2σT/κ. The accuracy of a Harman based approach, as implemented by the ZT-Scanner (TEMTE Inc.), applicable to the simultaneous measurement of the above TE properties, has been made possible by a self-contained calibration procedure, which is based on the availability of two samples of the same homogeneous material having different shape factors. It is of practical importance that this approach provides a simple procedure to obtain the calibration for the figure of merit ZT and the thermal conductivity in the temperature interval from 300 to 720 K. In addition, we show that a simplified Harman setup with no thermocouples attached to the sample can also be used for self-contained calibrated ZT measurements. It is concluded that the implemented steady-state approach decreases the relative error down to 1%–2% for ZT measurements and can be recommended for most applications not involving dynamical behavior. In particular, it is proposed that self-generated calibration samples can critically increase the quality and ease of comparison of TE measurements if they are adopted by the TE community. [ABSTRACT FROM AUTHOR]

In this work, a calibration of ruby, samarium-doped strontium tetraborate (Sm2+: SrB4O7), and samarium-doped yttrium aluminum garnet (Sm3+: YAG) using Raman and fluorescence spectra was conducted within the temperature range of 296–850 K and pressure range of 0–55 GPa. The obtained calibration can be applied independently for high-temperature or high-pressure conditions and described as the unit form of P = (A′/B′) × [(λ/λT)B′ − 1] with A′ = A (296 K) + A1 × (T − 296) + A2 × (T − 296)2, B′ = B(296 K) + B1 × (T – 296), and λT = λT (296 K) + ΔλT, where the specific parameters are provided in the main text. It was observed that for the λ1 line (5D0 → 7F0 transition, about 685.2 nm under ambient conditions, also known as the 0-0 line) of Sm2+: SrB4O7, the neglect of the temperature effect on the pressure coefficient may lead to an underestimation of pressure above 35–40 GPa, with a maximum deviation of approximately 2.5 GPa within the range of 55 GPa and 850 K. For Sm3+: YAG, it may introduce significant errors under the whole high temperature and high pressure range if the effect of temperature is ignored, that is, about 3.9 GPa for Y1 line (4G5/2 → 6H7/2 transition, about 617.8 nm under ambient conditions) and 4.6 GPa for Y2 line (4G5/2 → 6H7/2 transition, about 616.0 nm under ambient conditions) at 850 K. Comparing the three fluorescence pressure sensors, the ruby has the strongest signal intensity and highest temperature sensitivity, and the Sm2+: SrB4O7 and the Sm3+: YAG possess lower temperature sensibility, wider used temperature range, and better spectral quality under high temperature and high pressure (HTHP), especially Sm2+: SrB4O7, which has a sharp high-intensity single peak λ1, perhaps the most promising sensor for high P–T experiments. Therefore, in view of the potential deflections of fluorescence peaks of each pressure sensor under HTHP, we recommend utilizing the HTHP-corrected relationships for pressure calibration. [ABSTRACT FROM AUTHOR]

Optical second harmonic generation (SHG) can be employed to characterize the passivation quality of semiconducting material interfaces. The interface electric field (EDC) related to the existing charges at and near the interface, including the fixed oxide charges Qox, gives rise to the electric field induced second harmonic phenomenon. In this paper, we calibrate the SHG response for EDC measurement, using Al2O3/SiO2/Si(100) samples with different Qox. To perform this calibration, SHG and capacitance-voltage measurements (to access the electrical field of the samples) were made. The experimental results match well the simulated calibration curve, proving the potential of the SHG as stand-alone characterization technique for dielectric stacks on Si. [ABSTRACT FROM AUTHOR]

Colloidal probes are often used in force microscopy when the geometry of the tip–sample interaction should be well controlled. Their calibration requires an understanding of their mechanical response, which is very sensitive to the details of the force sensor consisting of a cantilever and the attached colloid. We present some analytical models to describe the dynamics of the cantilever and its load positioned anywhere along its length. The thermal noise calibration of such probes is then studied from a practical point of view, leading to correction coefficients that can be applied in standard force microscope calibration routines. Experimental measurements of resonance frequencies and thermal noise profiles of raw and loaded cantilevers demonstrate the validity of the approach. [ABSTRACT FROM AUTHOR]

BackgroundBrain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy.MethodsA 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice.ResultsOn the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours.ConclusionsIn a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.).

Polygenic scores (PGS) have emerged as the tool of choice for genomic prediction in a wide range of fields. We show that PGS performance varies broadly across contexts and biobanks. Contexts such as age, sex and income can impact PGS accuracy with similar magnitudes as genetic ancestry. Here we introduce an approach (CalPred) that models all contexts jointly to produce prediction intervals that vary across contexts to achieve calibration (include the trait with 90% probability), whereas existing methods are miscalibrated. In analyses of 72 traits across large and diverse biobanks (All of Us and UK Biobank), we find that prediction intervals required adjustment by up to 80% for quantitative traits. For disease traits, PGS-based predictions were miscalibrated across socioeconomic contexts such as annual household income levels, further highlighting the need of accounting for context information in PGS-based prediction across diverse populations.

OBJECTIVE: This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation. MATERIALS AND METHODS: We use data from 5 US claims and electronic health record databases and develop models for various outcomes in a major depressive disorder patient population. We externally validate all models in the other databases. We use a train-test split of 75%/25% and evaluate performance with discrimination and calibration. Statistical analysis for difference in performance uses Friedmans test and critical difference diagrams. RESULTS: Of the 840 models we develop, L1 and ElasticNet emerge as superior in both internal and external discrimination, with a notable AUC difference. BAR and IHT show the best internal calibration, without a clear external calibration leader. ElasticNet typically has larger model sizes than L1. Methods like IHT and BAR, while slightly less discriminative, significantly reduce model complexity. CONCLUSION: L1 and ElasticNet offer the best discriminative performance in logistic regression for healthcare predictions, maintaining robustness across validations. For simpler, more interpretable models, L0-based methods (IHT and BAR) are advantageous, providing greater parsimony and calibration with fewer features. This study aids in selecting suitable regularization techniques for healthcare prediction models, balancing performance, complexity, and interpretability.

Dynamic shockless compression experiments provide the ability to explore material behavior at extreme pressures but relatively low temperatures. Typically, the data from these types of experiments are interpreted through an analytic method called Lagrangian analysis. In this work, alternative analysis methods are explored using modern statistical methods. Specifically, Bayesian model calibration is applied to a new set of platinum data shocklessly compressed to 570 GPa. Several platinum equation-of-state models are evaluated, including traditional parametric forms as well as a novel non-parametric model concept. The results are compared to those in Paper I obtained by inverse Lagrangian analysis. The comparisons suggest that Bayesian calibration is not only a viable framework for precise quantification of the compression path, but also reveals insights pertaining to trade-offs surrounding model form selection, sensitivities of the relevant experimental uncertainties, and assumptions and limitations within Lagrangian analysis. The non-parametric model method, in particular, is found to give precise unbiased results and is expected to be useful over a wide range of applications. The calibration results in estimates of the platinum principal isentrope over the full range of experimental pressures to a standard error of 1.6%, which extends the results from Paper I while maintaining the high precision required for the platinum pressure standard. [ABSTRACT FROM AUTHOR]

A force plate is mainly used in biomechanics; it aims to measure the ground reaction force in a persons walking or standing position. In this study, a large-area force mat of the piezoresistance sensing type was developed, and a deep-learning-based weight measurement calibration method was applied to solve the problem in which measurements are not normalized because of physical limitations in hardware and signal processing. The test set was composed of the values measured at each point by weight and the value of the center of the pressure variable, and the measured value was predicted using a deep neural network (DNN) regression model. The calibration verification results show that the average weight errors range from a minimum of 0.06% to a maximum of 3.334%. This is simpler than the previous method, which directly measures the ratio of the resistance value to the measured weight of each sensor and derives an equation.

Purpose: To compare the linear and angular deviations of sites prepared for implants with the proposed 3D-printed sliding guide vs a 3D-printed conventional sleeveless static guide. Materials and Methods: This study consisted of two groups: a control group of 14 3D-printed conventional sleeveless static guides, and a test group of 37 3D-printed sliding guides. The guides were designed to drill 37 implant sites in each group. Linear and angular deviations were determined after scanning the drilled casts. Linear deviations were measured at the coronal and apical aspects of the drilled implant sites at 5-, 10-, 15-, and 20-mm distances. Results: There were no significant differences between the control and study groups regarding the coronal and 5-mm apical linear deviations. The angular and 10-, 15-, and 20-mm linear deviations of the 3D-printed sliding guide were significantly smaller than the control group. Conclusions: The proposed 3D-printed sliding guide could reduce the angular deviation of drilled implant sites from the planned positions, which is reflected in reducing the linear apical deviation of prepared implant sites longer than 5 mm. [ABSTRACT FROM AUTHOR]

Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. The proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.

Abstract: With the precipitous decline in response rates, researchers and pollsters have been left with highly nonrepresentative samples, relying on constructed weights to make these samples representative of the desired target population. Though practitioners employ valuable expert knowledge to choose what variables X must be adjusted for, they rarely defend particular functional forms relating these variables to the response process or the outcome. Unfortunately, commonly used calibration weights—which make the weighted mean of X in the sample equal that of the population—only ensure correct adjustment when the portion of the outcome and the response process left unexplained by linear functions of X are independent. To alleviate this functional form dependency, we describe kernel balancing for population weighting (kpop). This approach replaces the design matrix X with a kernel matrix, K encoding high-order information about X. Weights are then found to make the weighted average row of K among sampled units approximately equal to that of the target population. This produces good calibration on a wide range of smooth functions of X, without relying on the user to decide which X or what functions of them to include. We describe the method and illustrate it by application to polling data from the 2016 US presidential election.

Accurate calibration of the milling force coefficients is essential for predicting the milling forces and characterizing the milling state. Cutter run-out is an unavoidable phenomenon in end milling process, which affects the working state of the cutter and the machining accuracy of the work-piece. This paper proposes an optimized method based on the average force method to calibrate milling force coefficients and cutter run-out parameters in the end milling process. The entire calibration procedure is divided into three steps. First, the milling force coefficients are initially calibrated through the utilization of the average force method. Then, further optimization is carried out based on the coefficients calibrated by the average force method; average milling force equations are used, and a calibration procedure is presented by defining an objective function which is utilized to limit the average force error ratios between the calculated results and the measured results. Finally, the milling force coefficients obtained in the second step are used to calibrate the cutter run-out parameters; the instantaneous milling force equations are used, and a calibration procedure is also presented by defining a new objective function with the minimum sum of squared errors between the instantaneous prediction results and the instantaneous measurement results. In comparison to the experimentally measured average milling forces and instantaneous milling force curves, the proposed method exhibits significantly smaller mean force error ratios than those of the average force method, while demonstrating improved agreement with the simulated milling force curves. [ABSTRACT FROM AUTHOR]

The Dark Matter Particle Explorer (DAMPE) is a cosmic-ray detector as well as a pair-converting γ -ray telescope. The effective area, reflecting the geometrical cross-section area, the γ -ray conversion probability, and the photon selection efficiency, is important in γ -ray analyses. In this work, we find a significant time variation in the effective area, as large as ∼−4% yr−1 at 2 GeV for the high-energy trigger. We derive the data-based correction factors to the effective areas and apply corrections to both the effective areas and the exposure maps. The calibrated exposure can be ∼12% smaller than the Monte Carlo one on average at 2 GeV. The calibration is further verified using the observation of the Vela pulsar, showing the spectral parameters with the correction are more consistent with those in the Fermi-LAT catalog than the ones without correction. All the corrections are now implemented in the latest version of the DAMPE γ -ray analysis toolkit DmpST. [ABSTRACT FROM AUTHOR]

The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionization (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at |$k_\parallel \approx 0.5$| h Mpc |$^{-1}$|⁠. [ABSTRACT FROM AUTHOR]

Discrepancies between cosmological parameter estimates from cosmic shear surveys and from recent Planck cosmic microwave background measurements challenge the ability of the highly successful |$\Lambda$| CDM model to describe the nature of the Universe. To rule out systematic biases in cosmic shear survey analyses, accurate redshift calibration within tomographic bins is key. In this paper, we improve photo- z calibration via Bayesian hierarchical modeling of full galaxy photo- z conditional densities, by employing |${\it StratLearn}$|⁠ , a recently developed statistical methodology, which accounts for systematic differences in the distribution of the spectroscopic training/source set and the photometric target set. Using realistic simulations that were designed to resemble the KiDS + VIKING-450 data set, we show that |${\it StratLearn}$| -estimated conditional densities improve the galaxy tomographic bin assignment, and that our |${\it StratLearn}$| -Bayesian framework leads to nearly unbiased estimates of the target population means. This leads to a factor of |$\sim 2$| improvement upon often used and state-of-the-art photo- z calibration methods. Our approach delivers a maximum bias per tomographic bin of |$\Delta \langle z \rangle = 0.0095 \pm 0.0089$|⁠ , with an average absolute bias of |$0.0052 \pm 0.0067$| across the five tomographic bins. [ABSTRACT FROM AUTHOR]

This paper presents new method for traceability assurance of measurements performed using hybrid measuring systems built using one system that is based on tactile point measurement method (for example: coordinate measuring machine, articulated arm coordinate measuring machine, laser tracker system) and the second one that is based on optical field measurement method (for example: structured light scanners, digital image correlation systems). Within works described in this paper a series of tests aimed at determining task-specific errors for measurements performed using such composed systems were run. Measurement tasks for which such errors were determined include length measurements and measurements of form deviations (roundness, flatness, etc). Measurements were performed using material standards representing various shapes, dimensions and geometric relations. Measurements were run in different orientations and positions of the standards. Types of standards along with orientations and positions used were chosen basing on the guidelines of the ISO 10360 standard, parts 2, 5, 7, 8, 9, 10, 12, VDI/VDE 2634 and longtime experience of authors of this paper. At the end, results of performed measurements were checked for consistency with results of material standards calibration and values of task- specific maximum permissible errors were established. Guidelines for using developed method in other hybrid systems were also presented in the paper. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Methods Results Conclusions Periodontal disease (PD) screening models based on a self‐reported questionnaire were previously established and externally validated. The aim of the present study is to explore whether the screening models could be modified to improve prediction performance; this methodology is called ‘updating’.Updating the models for ‘total’ and ‘severe’ PD was performed using two datasets. One dataset from a previous study (n = 155) was used to explore the updating, and a second (n = 187, built for the current study) was used to validate whether updating improved performance. Updating was based on different statistical approaches, including model recalibration and revision. Discrimination and calibration were assessed after updating.For ‘total’ PD, the update based on model revision improved its performance. However, still low AUCs were found: 0.64 (0.56–0.73) and 0.61 (0.53–0.69) with corresponding O:E ratios 1.00 (0.80–1.23) and 0.92 (0.75–1.13) in the update and validation cohorts, respectively. For ‘severe’ PD, performance of the original model without update performed still the best; AUCs were 0.72 (0.61–0.83) and 0.75 (0.66–0.84) in the update and validation cohorts, respectively, with corresponding O:E ratios 0.60 (0.38–0.84) and 0.62 (0.42–0.87).The updating methodology did not further improve the performance of the original ‘severe’ PD screening model; it performed satisfactorily in the medical care setting. Despite updating attempts, the screening model for ‘total’ PD remained sub‐optimal. Screening for ‘severe’ PD can now be implemented in the medical care setting. [ABSTRACT FROM AUTHOR]

Individual differences are a prominent feature of normal color vision and range from variations in sensitivity to perception and color naming. Corrections for differences in spectral sensitivity are common, and there is growing interest in calibrating displays for the sensitivity of an individual observer. In contrast, few studies have explored calibrations for aspects of color appearance. We developed a technique for adjusting images based directly on an individual's hue percepts and illustrate the principle of the approach using a set of hue scaling functions measured previously for a large sample of color‐normal observers (Emery et al. PNAS 2023). Colors in an image are mapped onto the average scaling function to define the hue perceived by the average “standard observer.” This hue is then mapped back to the chromaticity that would elicit the same response in any individual observer. With this correction, different observers – each looking at physically different images calibrated for their own hue percepts – should in principle agree on the perceived colors. Adjustments of this kind could be easily implemented on standard displays, because they require only measures of hue percepts and not spectral sensitivity, and could lead to greater consistency in the perception and communication about color across individuals with potentially widely different perceptual experiences of color. [ABSTRACT FROM AUTHOR]

Accurately measuring the form of structured composite surfaces in situ is critical for advanced manufacturing in various engineering fields. However, challenges persist in achieving precision, miniaturization, and calibration using current structured light techniques. In this work, a hybrid structured light with compact configuration is proposed for the in-situ and embedded form metrology of structured composite surfaces. The proposed technique contains three subsystems: phase-measuring deflectometry (PMD), fringe projection profilometry (FPP), and stereo vision. The PMD subsystem accurately reconstructs the form data of specular surfaces based on the principle of structured light reflection, and the FPP subsystem measures rough surfaces by projecting structured light onto them. Output data from these subsystems are then stitched to reconstruct a complete form of the measured composite surfaces. The compact configuration is explored to reduce the system volume to improve the technique's portability and embedded measurement ability. With the stereo vision subsystem as an intermediary, a novel calibration method is applied for calculating the relations among the subsystems to improve the hybrid structured light system's calibration and data stitching accuracy between PMD and FPP subsystems. Three calibration tools are designed and manufactured for the proposed calibration technique. A portable metrology prototype based on the proposed hybrid structured light technique's principle and configuration is also developed and then calibrated using the novel calibration method. An embedded measurement experiment in a diamond turning machine demonstrates that the proposed techniques can achieve 400 nm form accuracy in specular surface measurement. Highlights: A novel hybrid structured light-based technique is proposed for the in-situ and embedded form metrology of structured composite surfaces. A beam splitter (BS)-based compact configuration is used to improve the technique' portability and embedded measurement ability. A novel calibration method is applied to calculate the relations among the subsystems using the stereo vision subsystem as an intermediary. [ABSTRACT FROM AUTHOR]

Introduction: Non-invasive cuffless blood pressure devices have shown promising results in accurately estimating blood pressure when comparing measurements at rest. However, none of commercially available or prototype cuffless devices have yet been validated according to the appropriate standards. The aim of the present study was to bridge this gap and evaluate the ability of a prototype cuffless device, developed by Aidee Health AS, to track changes in blood pressure compared to a non-invasive, continuous blood pressure monitor (Human NIBP or Nexfin) in a laboratory set up. The performance was evaluated according to the metrics and statistical methodology described in the ISO 81060-3:2022 standard. However, the present study is not a validation study and thus the study was not conducted according to the ISO 81060-3:2022 protocol, e.g., non-invasive reference and distribution of age not fulfilled. Method: Data were sampled continuously, beat-to-beat, from both the cuffless and the reference device. The cuffless device was calibrated once using the reference BP measurement. Three different techniques (isometric exercise, mental stress, and cold pressor test) were used to induce blood pressure changes in 38 healthy adults. Results: The mean difference (standard deviation) was 0.3 (8.7) mmHg for systolic blood pressure, 0.04 (6.6) mmHg for diastolic blood pressure, and 0.8 (7.9) mmHg for mean arterial pressure, meeting the Accuracy requirement of ISO 81060-3:2022 (≤6.0 (10.0) mmHg). The corresponding results for the Stability criteria were 1.9 (9.2) mmHg, 2.9 (8.1) mmHg and 2.5 (9.5) mmHg. The acceptance criteria for the Change requirement were achieved for the 85th percentile of ≤50% error for diastolic blood pressure and mean arterial pressure but were higher than the limit for systolic blood pressure (56% vs. ≤50%) and for all parameters for the 50th percentile (32%–39% vs. ≤25%). Conclusions: The present study demonstrated that the cuffless device could track blood pressure changes in healthy adults across different activities and showed promising results in achieving the acceptance criteria from ISO 81060-3:2022. [ABSTRACT FROM AUTHOR]

In the liquorice-soil composite shear test, flexible thick roots bend to create soil resistance which makes measurement results inaccurate. However, the liquorice pulling force characterises the contact strength of the liquorice-soil composite and can be used to study the root-soil interactions. This paper proposed a three-part modelling method to model the liquorice-soil composite at harvesting period. The mechanical parameters of soil particles were calibrated using the soil unconfined compressive strength test. The calibration results showed that the errors of peak force and peak displacement for soil unconfined compressive strength tests were 1.09% and 1.64%, respectively. The flexible liquorice model was constructed based on 3D scanning and particle filling methods, and the simulation model was calibrated based on compression properties. The relative errors in calibration of the flexible liquorice's radial and axial compression forces were 1.35% and 3.9%, respectively. Simplifying liquorice pulling force and liquorice surface area into a linear correlation effectively supports the general modelling method. The contact parameters between soil and liquorice were determined using liquorice pulling force as the target value, and the proportional calibration method was used to improve simulation efficiency. The calibration error for the liquorice pulling force is 4.39%. In addition, the results of the pulling force for the different surface areas show that the calibrated parameters are valid within a liquorice surface area of 0.0075–0.0181 m2. This study provided a general and accurate simulation method to the liquorice-soil composite, which can be used as the reference for modelling the long root-soil composite, and provide methodological support for developing root crop harvesters. • Modelling method to calibrate contact parameters of liquorice and soil particles. • The proportional calibration method was used to improve the modelling efficiency. • Fitted linear equation between the pulling force and the liquorice's surface area. • Calibrated model matched the physical properties of the harvesting liquorice plots. [ABSTRACT FROM AUTHOR]

We consider a parametric hypothesis test of the multiplicative distortion model with both a parametric global trend and a nonparametric local trend. In the model, neither the response variable nor the covariates can be directly observed, but are measured with multiplicative distortion measurement errors. Four calibration procedures with several estimation methods are used to construct the test statistics, namely, conditional mean calibration based test statistic, conditional absolute mean calibration based test statistic, conditional variance calibration based test statistic, and conditional absolute logarithmic calibration based test statistic. Asymptotic properties for these test statistics are established under the null hypothesis and alternative hypothesis. Monte Carlo simulation experiments are carried out to examine the performance of the proposed test statistics. A real example is analyzed to illustrate their practical usages. [ABSTRACT FROM AUTHOR]

In recent years, the teaching of experimental science and engineering has been revolutionized by the integration of smartphone sensors, which are widely used by a large portion of the population. Concurrently, interest in solar energy has surged. This raises the important question of how smartphone sensors can be harnessed to incorporate solar energy studies into undergraduate education. We provide comprehensive guidelines for using smartphone sensors in various conditions, along with detailed instructions on how to calibrate them with widely accessible clear-sky satellite data. This smartphone-based method is also compared with professional reference measurements to ensure consistency. This experiment can be easily conducted with most smartphones, basic materials, and a clear, open location over a few hours (methods). The findings demonstrate that smartphones, combined with simple resources, can accurately measure solar irradiance and support experiments on solar radiation physics, atmospheric interactions, and variations in solar energy across locations, cloud cover, and time scales. This approach provides a practical and accessible tool for studying solar energy, offering an innovative and engaging method for measuring solar resources. [ABSTRACT FROM AUTHOR]

Chemical analysis adopts a calibration curve to establish the relationship between the measuring technique's response and the target analyte's standard concentration. The calibration equation is established using regression analysis to verify the response of a chemical instrument to the known properties of materials that served as standard values. An adequate calibration equation ensures the performance of these instruments. There are two kinds of calibration equations: classical equations and inverse equations. For the classical equation, the standard values are independent, and the instrument's response is dependent. The inverse equation is the opposite: the instrument's response is the independent value. For the new response value, the calculation of the new measurement by the classical equation must be transformed into a complex form to calculate the measurement values. However, the measurement values of the inverse equation could be computed directly. Different forms of calibration equations besides the linear equation could be used for the inverse calibration equation. This study used measurement data sets from two kinds of humidity sensors and nine data sets from the literature to evaluate the predictive performance of two calibration equations. Four criteria were proposed to evaluate the predictive ability of two calibration equations. The study found that the inverse calibration equation could be an effective tool for complex calibration equations in chemical analysis. The precision of the instrument's response is essential to ensure predictive performance. The inverse calibration equation could be embedded into the measurement device, and then intelligent instruments could be enhanced. [ABSTRACT FROM AUTHOR]

Purpose: To assess how well torque‐limiting devices (TLDs) are known and used by European dentists, and their adherence to screw tightening protocols and screw loosening occurrence through a survey, including the correlation between the dental specialty‐of‐interest and the recognition, the tightening protocol used, and between the calibration and the occurrence of screw loosening. Materials and methods: A 10‐question survey was distributed to dentists to collect data on their specialty‐of‐interest, TLD usage, knowledge on TLDs, calibration, the term "preload," tightening speed, tightening protocols used, and occurrence of screw loosening. Pearson test was used for correlation analysis between the specialty‐of‐interest and the recognition‐based questions, the tightening protocol used, and between the calibration and the frequency of screw loosening. Results: Of 422 respondents, 24% calibrated their TLDs, 27% knew the term "preload," 76% selected the correct location to read on TLDs, and 6% was aware of the effect of tightening speed. The correlation between the specialty‐of‐interest and the recognition‐based questions was nonsignificant (p <.05) but was significant for used tightening protocol (p <.001). The correlation between the calibration and the occurrence of screw loosening was nonsignificant (p = 0.16). Tightening protocols' effect on screw loosening was similar, which was mostly observed less than once a year (p <.001). Conclusions: A lack in dentists' knowledge was found on calibration, the term preload, and the effect of tightening speed, which were not impacted by the dentists' specialty‐of‐interest, which affected the preferred tightening protocol. The tightening protocol and calibration did not impact the occurrence of screw loosening, which was mostly observed less than once a year. [ABSTRACT FROM AUTHOR]

Radar polarimetry is a technique that can be used to enhance target detection, identification and classification; however, the quality of these measurements can be significantly influenced by the characteristics of the radar antenna. For an accurate and reliable system, the calibration of the antenna is vitally important to mitigate these effects. This study presents a methodology to calibrate Ultra-Wideband (UWB) dual-polarised antennas in the near-field using a thin elongated metallic cylinder as the calibration object. The calibration process involves measuring the scattering matrix of the metallic cylinder as it is rotated, in this case producing 100 distinct scattering matrices from which the calibration parameters are derived, facilitating a robust and stable solution. The calibration procedure was tested and validated using a Vector Network Analyser (VNA) and two quad-ridged antennas, which presented different performance levels. The calibration methodology demonstrated notable improvements, aligning the performance of both functioning and under-performing antennas to equivalent specifications. Mid-band validation measurements indicated minimal co-polar channel imbalance (<0.3 dB), low phase error (<0.8°) and improved cross-polar isolation (≈48 dB). [ABSTRACT FROM AUTHOR]

Rice farming in Italy accounts for about 50% of the EU's rice area and production. Precision agriculture has entered the scene to enhance sustainability, cut pollution, and ensure food security. Various studies have used remote sensing tools like satellites and drones for multispectral imaging. While Sentinel-2 is highly regarded for precision agriculture, it falls short for specific applications, like at the "Riserva San Massimo" (Gropello Cairoli, Lombardia, Northern Italy) rice farm, where irregularly shaped crops need higher resolution and frequent revisits to deal with cloud cover. A prior study that compared Sentinel-2 and the higher-resolution PlanetScope constellation for vegetative indices found a seasonal miscalibration in the Normalized Difference Vegetation Index (NDVI) and in the Normalized Difference Red Edge Index (NDRE). Dr. Agr. G.N. Rognoni, a seasoned agronomist working with this farm, stresses the importance of studying the radiometric intercalibration between the PlanetScope and Sentinel-2 vegetative indices to leverage the knowledge gained from Sentinel-2 for him to apply variable rate application (VRA). A high-resolution SkySat image, taken almost simultaneously with a pair of Sentinel-2 and PlanetScope images, offered a chance to examine if the irregular distribution of vegetation and barren land within rice fields might be a factor in the observed miscalibration. Using an unsupervised pixel-based image classification technique on SkySat imagery, it is feasible to split rice into two subclasses and intercalibrate them separately. The results indicated that combining histograms and agronomists' expertise could confirm SkySat classification. Moreover, the uneven spatial distribution of rice does not affect the seasonal miscalibration object of past studies, which can be adjusted using the methods described here, even with images taken four days apart: the first method emphasizes accuracy using linear regression, histogram shifting, and histogram matching; whereas the second method is faster and utilizes only histogram matching. [ABSTRACT FROM AUTHOR]

An overview of the status of the research in analytical figures of merit is provided, including all calibration scenarios from univariate to multivariate and multiway analytical protocols. Both linear and nonlinear multivariate models are considered. Starting with the simplest multivariate model, inverse least‐squares regression, the basic concepts of sensitivity, sample leverage, and limit of detection are introduced. The extension to other multivariate models is discussed, as well as to nonlinear models based on radial basis functions, kernel partial least‐squares, and multilayer feed‐forward artificial neural networks. Finally, multiway calibration models are discussed, including multilinear decomposition models such as parallel factor analysis (PARAFAC) and multivariate curve resolution–alternating least‐squares (MCR‐ALS). In the latter case, recent developments concerning the pervasive phenomenon of rotational ambiguity are discussed. Unfinished works and areas where further research efforts are needed to develop closed‐form expressions and to fully understand their meaning are included. [ABSTRACT FROM AUTHOR]

The performance of multivariate calibration models ŷ = f(x) for the prediction of a numerical property y from a set of x‐variables depends on the type of scaling of the x‐variables. Common scaling methods are autoscaling (dividing the centered x by its standard deviation s) and Pareto scaling (dividing the centered x by sP with P = 0.5). The adjusted Pareto scaling presented here varies the exponent P between 0 (no scaling) and 1 (autoscaling) with the aim of obtaining an optimum prediction performance for ŷ. Related scaling methods based on the variable spread are range scaling and vast scaling; while level scaling is based on the location (central value) of the variable. These scaling methods and robust versions are compared for models created by partial least‐squares (PLS) regression. The applied strategy repeated double cross validation (rdCV) evaluates the model performance for test set objects and considers its variability. Results with three data sets from chemistry show: (a) the efficacy of the different scaling methods depends on the data structure; (b) optimization of the Pareto exponent P is recommended; (c) range scaling or vast scaling may be better than adjusted Pareto scaling; (d) in general a heuristic search for the best scaling method is advisable. Overall, the consideration of different variants of scaling allow for a flexible adjustment of the variable contributions to the calibration model. [ABSTRACT FROM AUTHOR]

Decision making in nuclear emergency response involves significant uncertainty, time pressure, and high stakes. The stress and ambiguity can result in the use of heuristic decision making strategies, which may sometimes result in poor decision outcomes. As complex information systems are often used in emergency operations, this research aims at examining linkages between design choices in computer displays and cognitive biases. This paper reports a literature review covering a set of themes that include framing effects, facilitating balanced perspectives of information, automation transparency, and the role of meta-information in conveying significance. The review provides evidence from the literature showing the importance of display design in mitigating the emergence of cognitive biases in decision making tasks. The review resulted in 11 guidelines for the design of computer displays in safety-critical applications, and directions are provided for future research. A theme that connects many of these guidelines is that providing the big picture can mitigate many types of biases, which often should encompass self-reflection and calibration of appropriate confidence levels. It can be concluded that over time, the potential for biasing users can be interwoven within computer displays if debiasing is not taken into consideration early in the design process. [ABSTRACT FROM AUTHOR]

Cryogenic electron microscopy (cryoEM) is a rapidly growing structural biology modality that has been successful in revealing molecular details of biological systems. However, unlike established biophysical and analytical techniques with calibration standards, cryoEM has lacked comprehensive biological test samples. Here, a cryoEM calibration sample consisting of a mixture of compatible macromolecules is introduced that can not only be used for resolution optimization, but also provides multiple reference points for evaluating instrument performance, data quality and image‐processing workflows in a single experiment. This combined test specimen provides researchers with a reference point for validating their cryoEM pipeline, benchmarking their methodologies and testing new algorithms. [ABSTRACT FROM AUTHOR]

Background: Intraoperative conversion to open surgery is an adverse event during minimally invasive distal pancreatectomy (MIDP), associated with poor postoperative outcomes. The aim of this study was to develop a model capable of predicting conversion in patients undergoing MIDP. Methods: A total of 352 patients who underwent MIPD were included in this retrospective analysis and randomly assigned to training and validation cohorts. Potential risk factors related to open conversion were identified through a literature review, and data on these factors in our cohort was collected accordingly. In the training cohort, multivariate logistic regression analysis was performed to adjust the impact of confounding factors to identify independent risk factors for model building. The constructed model was evaluated using the receiver operating characteristics curve, decision curve analysis (DCA), and calibration curves. Results: Following an extensive literature review, a total of ten preoperative risk factors were identified, including sex, BMI, albumin, smoker, size of lesion, tumor close to major vessels, type of pancreatic resection, surgical approach, MIDP experience, and suspicion of malignancy. Multivariate analysis revealed that sex, tumor close to major vessels, suspicion of malignancy, type of pancreatic resection (subtotal pancreatectomy or left pancreatectomy), and MIDP experience persisted as significant predictors for conversion to open surgery during MIDP. The constructed model offered superior discrimination ability compared to the existing model (area under the curve, training cohort: 0.921 vs. 0.757, P < 0.001; validation cohort: 0.834 vs. 0.716, P = 0.018). The DCA and the calibration curves revealed the clinical usefulness of the nomogram and a good consistency between the predicted and observed values. Conclusion: The evidence-based prediction model developed in this study outperformed the previous model in predicting conversions of MIDP. This model could contribute to decision-making processes surrounding the selection of surgical approaches and facilitate patient counseling on the conversion risk of MIDP. [ABSTRACT FROM AUTHOR]

The use of distributed acoustic sensing (DAS) to acquire vertical seismic profiles (VSPs) is becoming increasingly popular as the use of the fibre-optic cable as the sensor enables the full depth of the well to be acquired simultaneously. A major drawback in the use of DAS is tying the distance along the cable to its position within the well. In this paper, we show how the cable position can be determined over its full length using a dynamic time-warping algorithm applied to a corridor stack and synthetic seismogram. To constrain the result to ensure that result is physically realistic, we employ a constant warping window and/or a newly introduced bunching window. [ABSTRACT FROM AUTHOR]