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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]

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]

Equations of state (EOSs) are typically represented as physics-informed models with tunable parameters that are adjusted to replicate calibration data as closely as possible. Uncertainty quantification (UQ) allows for the development of an ensemble of EOS parameters that are consistent with the calibration data instead of a single EOS. In this work, we perform UQ for the reactant and product EOSs for a variety of high explosives (HEs). In doing so, we demonstrate a strategy for dealing with heterogeneous (both experimental and calculated) data. We also use a statistical distance metric to quantify the differences between the various HEs using the UQ results. [ABSTRACT FROM AUTHOR]

Dynamic compression experiments in condensed matter are of interest in part because they provide opportunities to examine material response under extreme conditions; however, the inference of material behavior from dynamic experiments is challenging in the presence of phase transitions exhibiting kinetic processes. Demonstrating an approach to quantitative interpretation of such dynamic experiments, we present a Bayesian model calibration of strength and phase transformation parameters to data drawn from pulsed power and gas gun shot experiments. The posterior predictions of the Bayesian model capture the experimental measurements and account for the various uncertainties in the experimental configurations. This holistic approach to model calibration utilizing multiple types of experimental data identifies important cross correlations among kinetics, strength, and the phase boundary. Improved insight into potential sources of current model form error is provided by comparing the differences between calibrations against different subsets of the experimental data. [ABSTRACT FROM AUTHOR]

In recent years, there are numerous articles about computer model calibration. But these articles mainly focus on continuous output variables for the convenience of the derivation. However, there are some other types of output data in the application area, such as binary data and count data, the former of which has been investigated very well in the area of computer model calibration and applied to cell adhesion study successfully. Inspired by the existing methods for binary data, we propose a model calibration procedure for count response, and derive the consistency and asymptotic normality of the proposed estimator of the calibration parameters by the theory of empirical process. Some simulations are conducted to verify the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

Introduction: Intraoperative indocyanine green fluorescence angiography (ICGFA) aims to reduce colorectal anastomotic complications. However, signal interpretation is inconsistent and confounded by patient physiology and system behaviours. Here, we demonstrate a proof of concept of a novel clinical and computational method for patient calibrated quantitative ICGFA (QICGFA) bowel transection recommendation. Methods: Patients undergoing elective colorectal resection had colonic ICGFA both immediately after operative commencement prior to any dissection and again, as usual, just before anastomotic construction. Video recordings of both ICGFA acquisitions were blindly quantified post hoc across selected colonic regions of interest (ROIs) using tracking-quantification software and computationally compared with satisfactory perfusion assumed in second time-point ROIs, demonstrating 85% agreement with baseline ICGFA. ROI quantification outputs detailing projected perfusion sufficiency-insufficiency zones were compared to the actual surgeon-selected transection/anastomotic construction site for left/right-sided resections, respectively. Anastomotic outcomes were recorded, and tissue lactate was also measured in the devascularised colonic segment in a subgroup of patients. The novel perfusion zone projections were developed as full-screen recommendations via overlay heatmaps. Results: No patient suffered intra- or early postoperative anastomotic complications. Following computational development (n = 14) the software recommended zone (ROI) contained the expert surgical site of transection in almost all cases (Jaccard similarity index 0.91) of the nine patient validation series. Previously published ICGFA time-series milestone descriptors correlated moderately well, but lactate measurements did not. High resolution augmented reality heatmaps presenting recommendations from all pixels of the bowel ICGFA were generated for all cases. Conclusions: By benchmarking to the patient's own baseline perfusion, this novel QICGFA method could allow the deployment of algorithmic personalised NIR bowel transection point recommendation in a way fitting existing clinical workflow. [ABSTRACT FROM AUTHOR]

Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration. Med. Sci. Sports Exerc., Vol. 56, No. 6, pp. 1196-1207, 2024. Introduction: Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 yr. Methods: Children (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin's concordance correlation coefficient (CCC). Results: Mean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95. Conclusions: The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of equifinality in agent-based modeling (ABM) by introducing a novel sequential calibration approach. Equifinality arises when multiple models equally fit observed data, risking the selection of an inaccurate model. In the context of ABM, such a situation might arise due to limitations in data, such as aggregating observations into coarse spatial units. It can lead to situations where successfully calibrated model parameters may still result in reliability issues due to uncertainties in accurately calibrating the inner mechanisms. To tackle this, we propose a method that sequentially calibrates model parameters using diverse outcomes from multiple datasets. The method aims to identify optimal parameter combinations while mitigating computational intensity. We validate our approach through indoor pedestrian movement simulation, utilizing three distinct outcomes: (1) the count of grid cells crossed by individuals, (2) the number of people in each grid cell over time (fine grid) and (3) the number of people in each grid cell over time (coarse grid). As a result, the optimal calibrated parameter combinations were selected based on high test accuracy to avoid overfitting. This method addresses equifinality while reducing computational intensity of parameter calibration for spatially explicit models, as well as ABM in general. [ABSTRACT FROM AUTHOR]

Crop simulation models are valuable tools for decision making regarding evaluation and crop improvement under different field conditions. CSM-CROPGRO model integrates genotype, environment and crop management portfolios to simulate growth, development and yield. Modeling the safflower response to varied climate regimes are needed to strengthen its productivity dynamics. The main objective of the study was to evaluate the performance of DSSAT-CSM-CROPGRO-Safflower (Version 4.8.2) under diverse climatic conditions. The model was calibrated using the field observations for phenology, biomass and safflower grain yield (SGY) of the year 2016–17. Estimation of genetic coefficients was performed using GLUE (Genetic Likelihood Uncertainty Estimation) program. Simulated results for days to flowering, maturity, biomass at flowering and maturity and SGY were predicted reasonably with good statistical indices. Model evaluation results elucidate phenological events with low root mean square error (6.32 and 6.52) and high d-index (0.95 and 0.96) for days to flowering and maturity respectively for all genotypes and climate conditions. Fair prediction of safflower biomass at flowering and maturity showed low RMSE (887.3 and 564.3 kg ha−1) and high d-index (0.67 and 0.93) for the studied genotypes across the environments. RMSE for validated safflower grain yield (101.8 kg ha−1) and d-index (0.95) depicted that model outperformed for all genotypes and growing conditions. Longer appropriate growing conditions at NARC-Islamabad took optimal duration to assimilate photosynthetic products lead to higher grain yield. Safflower resilience to different environments showed that it can be used as an alternate crop for different agroecological regions. Furthermore, CROPGRO-Safflower model can be used as tool to further evaluate inclusion of safflower in the existing cropping systems of studied regions. [ABSTRACT FROM AUTHOR]

In this study, the Discrete Element Method (DEM) was employed as a computational approach to analyse the bulk behaviour of wheat (Triticum aestivum L., cv. 'Pionier') under compressive loading. A laboratory compression apparatus was utilised as a basis for the acquisition of data from compression tests. To ensure accurate DEM predictions, the physical, mechanical, and interaction properties of wheat kernels were experimentally determined for two different moisture contents (12.4% and 25.4% w.b.). Five particle models were constructed to represent the geometric shape of the kernel, with the model composed of five sub-spheres identified as the most suitable, ensuring an optimal balance between computational complexity and simulation accuracy. The bulk compression results from DEM simulations were compared to experimental data, revealing a good agreement for volumetric strain, bulk density increase, and bulk compressibility within the specified range of applicability. Simulations provided valuable insights into temporal and spatial variations of forces and deformations acting on individual particles, thereby enhancing the understanding of bulk behaviour at different compression levels. The moisture content was found to significantly affect the particle compressive forces, deformation capabilities, and bulk compression characteristics. In conclusion, this study demonstrated the great potential of DEM in predicting the bulk behaviour of wheat under compression, providing valuable information for practical storage and handling processes. • Bulk behaviour of wheat during compressive loading was studied using DEM. • DEM-relevant modelling parameters were determined experimentally. • Bulk volumetric strain, bulk density and compressibility were investigated. • Mechanical response of particles under bulk compression was analysed. • Moisture content effect on product properties and compression characteristics was examined. [ABSTRACT FROM AUTHOR]

The fast and accurate provision of within-season data of green area index (GAI) and total N uptake (total N) is the basis for crop modeling and precision agriculture. However, due to rapid advancements in multispectral sensors and the high sampling effort, there is currently no existing reference work for the calibration of one UAV (unmanned aerial vehicle)-based multispectral sensor to GAI and total N for silage maize, winter barley, winter oilseed rape, and winter wheat. In this paper, a practicable calibration framework is presented. On the basis of a multi-year dataset, crop-specific models are calibrated for the UAV-based estimation of GAI throughout the entire growing season and of total N until flowering. These models demonstrate high accuracies in an independent evaluation over multiple growing seasons and trial sites (mean absolute error of 0.19–0.48 m2 m−2 for GAI and of 0.80–1.21 g m−2 for total N). The calibration of a uniform GAI model does not provide convincing results. Near infrared-based ratios are identified as the most important component for all calibrations. To account for the significant changes in the GAI/ total N ratio during the vegetative phase of winter barley and winter oilseed rape, their calibrations for total N must include a corresponding factor. The effectiveness of the calibrations is demonstrated using three years of data from an extensive field trial. High correlation of the derived total N uptake until flowering and the whole-season radiation uptake with yield data underline the applicability of UAV-based crop monitoring for agricultural applications. [ABSTRACT FROM AUTHOR]

The 100-m X-ray Test Facility (100XF) was developed according to the calibration requirement of the first X-ray astronomical satellite Insight-HXMT in china. After that, the 100XF has provided calibration services for other X-ray astronomical satellites. In order to test the energy response matrices of space-borne detectors and to extend the capability of 100XF, a monochromator based on channel-cut crystals is developed. So far, 3.0-20.7 keV monochromatic X-rays have been realized successfully with Si(111) crystal. The monochromaticity (Δ E FWHM /E) is better than 0.6% @5.9 keV, the flux stability can reach 2.6% in about one hour. The energy stability is particularly good, the variation is 0.05% in about one hour. Further more, the beam spot spreads in the plane orthogonal to the rocking direction after a propagation of 100-meter, which is sufficient and useful for testing detectors with large sensitive area. In this paper, we present the research and establishment of the X-ray monochromator at the 100XF. [ABSTRACT FROM AUTHOR]

Background: To minimize measurement uncertainties and create seamless procedures between tests and simulations for the characterization and prediction of damage in large scale structures, a system capable of monitoring the quantities of interest at different scales throughout the test is required. Objective: The aim of this work is to develop a multiview DIC framework at varying scales in which kinematic fields are expressed on a unique mesh. Methods: A three-point flexural test was performed on a concrete beam and the images acquired by three different cameras were used to perform DIC calculations. Results: Displacement and strain fields were measured using mono and multiview implementations; their associated uncertainties were assessed. Damage initiation and growth within the sample was quantified based on the standard displacement uncertainty. Conclusion: The reported results show that the proposed method reduced the associated displacement uncertainties. The onset and propagation of damage was successfully quantified. [ABSTRACT FROM AUTHOR]

Selecting a "useful" model among a set of competing candidate models is a common problem in many disciplines and particularly in engineering applications. For a modeller, the best model fulfills a certain purpose best (e.g., the ability to accurately predict new data, good interpolation, precise estimates of lower/upper percentiles, etc.), which is typically assessed by comparing model simulations to the observed data. Approximate Bayesian computation (ABC) which is a statistical learning technique is used in this work for reliability model selection and parameter calibration using small/moderate fatigue life data sets. This is always the case in material fatigue due to the high cost and low efficiency of fatigue tests which are bottleneck problem when designing components/structures with regard to fatigue. The ABC is a likelihood‐free based method which means that it needs only a generative model to simulate the data. The proposed method provides a formal rank of the competing reliability models by eliminating gradually the least likely models in a parsimonious manner. It is flexible since it offers the possibility to use different metrics to measure the discrepancy between simulated and observed data. The choice of the appropriate distance function depends essentially on the purpose of model selection. Through various examples, it has been demonstrated that the ABC method has a number of very attractive properties, which makes it especially useful in probabilistic risk assessment and reliability analysis. [ABSTRACT FROM AUTHOR]

The study described here was aimed at assessing the capability of quantitative ultrasound (QUS) based on the backscatter coefficient (BSC) for classifying disease states, such as breast cancer response to neoadjuvant chemotherapy and quantification of fatty liver disease. We evaluated the effectiveness of an in situ titanium (Ti) bead as a reference target in calibrating the system and mitigating attenuation and transmission loss effects on BSC estimation. Traditional BSC estimation methods require external references for calibration, which do not account for ultrasound attenuation or transmission losses through tissues. To address this issue, we used an in situ Ti bead as a reference target, because it can be used to calibrate the system and mitigate the attenuation and transmission loss effects on estimation of the BSC. The capabilities of the in situ calibration approach were assessed by quantifying consistency of BSC estimates from rabbit mammary tumors (N = 21). Specifically, mammary tumors were grown in rabbits and when a tumor reached ≥1 cm in size, a 2 mm Ti bead was implanted in the tumor as a radiological marker and a calibration source for ultrasound. Three days later, the tumors were scanned with an L-14/5 38 array transducer connected to a SonixOne scanner with and without a slab of pork belly placed on top of the tumors. The pork belly acted as an additional source of attenuation and transmission loss. QUS parameters, specifically effective scatterer diameter (ESD) and effective acoustic concentration (EAC), were calculated using calibration spectra from both an external reference phantom and the Ti bead. For ESD estimation, the 95% confidence interval between measurements with and without the pork belly layer was 6.0, 27.4 using the in situ bead and 114, 135.1 with the external reference phantom. For EAC estimation, the 95% confidence intervals were –8.1, 0.5 for the bead and –41.5, –32.2 for the phantom. These results indicate that the in situ bead method has reduced bias in QUS estimates because of intervening tissue losses. The use of an in situ Ti bead as a radiological marker not only serves its traditional role but also effectively acts as a calibration target for QUS methods. This approach accounts for attenuation and transmission losses in tissue, resulting in more accurate QUS estimates and offering a promising method for enhanced disease state classification in clinical settings. [ABSTRACT FROM AUTHOR]

• The calibration problem of the general acupuncture robot is analyzed. • A simultaneous offline calibration method for problems defined with multiple closed kinematic chains is proposed. • A multi-objective optimization model covering all coupling components and constraints is constructed and solved. • A real-world platform with a customized calibration board is built. • The proposed method has higher calibration accuracy and convergence speed. Acupuncture robot is a new-era product combining traditional acupuncture and cutting-edge technology. The calibration of the vision system and the acupuncture mechanism is a crucial prerequisite for humanoid acupuncture control, which has not yet been explored. In this paper, a simultaneous offline calibration method is proposed for acupuncture robots. Analysis reveals that its calibration problem is defined with three closed kinematic chains, while the typical problems cover only a single chain. Decoupling them, a Kronecker product-based method is deduced to access the closed-form rotation component. As opposed to quaternion-based methods, it only experiences linear complexity in sign ambiguity, which can produce a more precise solution in finite time. Further, a simultaneous optimization model that encompasses all components is established, which can be solved with stochastic gradient descent-based methods. It is free of truncation errors and thus has higher calibration accuracy and convergence speed. Besides, the impairment in error propagation between different closed kinematic chains is mitigated compared to step-by-step methods. Finally, simulations and experiments are carried out. Notably, the proposed method can be easily extended to other robot calibration problems with multiple closed kinematic chains. [ABSTRACT FROM AUTHOR]

Calibration of 22 gamma camera units was performed at 15 hospitals in southern and western Sweden to estimate 137Cs contamination in humans in a supine static geometry, with a new developed calibration protocol and phantom. The minimum detectable activities (MDAs) and the estimated committed effective doses (CEDs) were calculated for each calibration. Generic calibration factors were calculated for five predetermined groups based on the detector type and manufacturer. Group 1 and 2 included NaI-based gamma cameras from General Electrics (GEs) with a crystal thickness of 5/8′′ and 3/8′′ respectively. Group 3 and 4 included NaI-based gamma cameras from Siemens Healthineers with a crystal thickness of 3/8′′, with a similar energy window as the GE NaI-based cameras and a dual window respectively. Group 5 included semiconductor-based gamma cameras from GE with a CdZnTe (CZT) detector. The generic calibration factors were 60.0 cps kBq−1, 52.3 cps kBq−1, 50.3 cps kBq−1, 53.2 cps kBq−1 and 48.4 cps kBq−1 for group 1, 2, 3, 4, and 5 respectively. The MDAs ranged between 169 and 1130 Bq for all groups, with measurement times of 1–10 min, corresponding to a CED of 4.77–77.6 μ Sv. A dead time analysis was performed for group 1 and suggested a dead time of 3.17 μ s for 137Cs measurements. The dead time analysis showed that a maximum count rate of 232 kcps could be measured in the calibration geometry, corresponding to a CED of 108–263 mSv. It has been shown that semiconductor-based gamma cameras with CZT detectors are feasible for estimating 137Cs contamination. The generic calibration factors derived in this study can be used for gamma cameras of the same models in other hospitals, for measurements in the same measurement geometry. This will increase the measurement capability for estimating internal 137Cs contamination in the recovery phase following radiological or nuclear events. [ABSTRACT FROM AUTHOR]

In this study, we assess the homogeneity of aerosol optical depth (AOD) between the sun photometer networks Global Atmospheric Watch-Precision Filter Radiometer (GAW-PFR) and European Skynet Radiometers network (ESR) at the 2 common wavelengths of their main instruments 500 nm and 870 nm. The main focus of the work is on evaluating the effect of the Improved Langley calibration method, (ILP) used by SKYNET and investigating the factors affecting its performance. We used data from three intercomparison campaigns that took place in the period 2017–2021. Each campaign has two phases in two locations. One is mountainous rural (Davos, Switzerland) and the other urban (Rome, Italy). Our analysis shows that the AOD differences due to post processing and instrument differences are minor. The major factor leading to AOD differences is the calibration method where we found a systematic underestimation of AOD compared to GAW-PFR due to an underestimation in the ILP calibration. The calibration and AOD differences are smaller in Davos where at 870 nm the traceability criteria are satisfied and at 500 nm the median differences are below 0.01. In Rome at 500 nm the AOD median differences per campaign are between 0.015–0.035. Attempting to explain the differences we found no association between the calibration performance and the level or the variability of the aerosol properties. We also conducted a sensitivity study, which shows that part of the difference can be potentially explained by errors in the assumed surface albedo and instrument solid view angle provided as inputs to the ILP code (based on Skyrad pack 4.2). Our findings suggest that the ILP method is mainly sensitive to the measured sky radiance. The calibration underestimation is probably caused by an error on the retrieved scattering aerosol optical depth (sc-AOD) through the sky radiance inversion. Using an alternative retrieval method (Skyrad MRI pack version 2) to derive sc-AOD and repeat ILP calibration, we found no significant differences between the retrieved sc-AOD nor systematic increase of the calibrations. The potential error may be a result of the forward model assumptions, To conclude, calibration of sun photometers on site offers the advantage of avoiding instrument shipments and data gaps. However, ILP shows larger uncertainty and significant systematic difference compared to the traditional Langley calibration performed under low and constant AOD conditions at high altitude sites, due to the uncertainties of the calibration method and the input parameters needed for it. In the following sections we report on results on the AOD retrievals of several instruments in different environments using different principles in their calibration methods. We also perform an investigation to explain the causes of differences. [ABSTRACT FROM AUTHOR]

Accurate calibration of a Stewart platform is important for their precise and efficient operation. However, the calibration of these platforms using forward kinematics is a challenge for researchers because forward kinematics normally generates multiple feasible and unfeasible solutions for any pose of the moving platform. The complex kinematic relations among the six actuator paths connecting the fixed base to the moving platform further compound the difficulty in establishing a straightforward and efficient calibration method. The authors developed a new forward kinematics-based calibration method using Denavit-Hartenberg (DH) convention and used the Stewart platform "Tiger 66.1" developed in their lab for experimenting with the photogrammetry-based calibration strategies described in this paper. This system became operational upon completion of construction, marking its inaugural use. The authors used their calibration model for estimating the errors in the system and adopted three compensation options or strategies as per least-square method to improve the accuracy of the system. These strategies leveraged a high-resolution digital camera and off-the-shelf software to capture the poses of the moving platform's center. This process is non-invasive and does not need any additional equipment to be attached to the hexapod or any alteration of the hexapod hardware. This photogrammetry-based calibration process involves multiple high-resolution images from different angles to measure the position and orientation of the platform center in the three-dimensional space. The target poses and actual poses are then compared, and the error compensations are estimated using the least-square methods to calculate the predicted poses. Results from each of the three compensation approaches demonstrated noticeable enhancements in platform pose accuracies, suggesting room for further improvements. Given that "Tiger 66.1" is based on the general Stewart platform structure, the proposed calibration method holds promise for extension to machines operating on similar principles where non-invasive calibration is desirable. This study contributes to advancing the field of Stewart platform calibration, paving the way for more precise and efficient applications in various domains. [ABSTRACT FROM AUTHOR]

AbstractThis article proposes a new kriging that has a rational form. It is shown that the generalized least squares estimator of the mean from rational kriging is much more well behaved than that of ordinary kriging. Parameter estimation and uncertainty quantification for rational kriging are proposed using a Gaussian process framework. A generalized version of rational kriging is also proposed, which includes ordinary and rational kriging as special cases. Extensive simulations carried out over a wide class of functions show that the generalized rational kriging performs on par or better than both ordinary and rational kriging in terms of prediction and uncertainty quantification. The only extra step needed for generalized rational kriging over ordinary kriging is the computation of Perron eigenvector of an augmented correlation matrix which can be computed in near linear time and therefore, its overall computational complexity is no more than that of ordinary kriging. The potential applications of the new kriging methods in the emulation of computationally expensive models and model calibration problems are illustrated with real and simulated examples. [ABSTRACT FROM AUTHOR]

Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process. [ABSTRACT FROM AUTHOR]

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added. [ABSTRACT FROM AUTHOR]

A phenomenological material model has been developed to facilitate the efficient numerical analysis of fiber-reinforced high-performance concrete (HPC). The formulation integrates an elasto-plastic phase-field model for simulating fractures within the HPC matrix, along with a superimposed one-dimensional elasto-plasticity model that represents the behavior of the embedded fibers. The Drucker–Prager plasticity and one-dimensional von-Mises plasticity formulations are incorporated to describe the nonlinear material behavior of both the HPC matrix and the fibers, respectively. Specific steps are undertaken during the development and calibration of the phenomenological material model. In the initial step, an experimental and numerical analysis of the pullout test of steel fibers embedded in an HPC matrix is conducted. This process is used to calibrate the micro-mechanical model based on the elasto-plastic phase-field formulation for fracture. In the subsequent step, virtual experiments based on an ellipsoidal unit cell, also with the resolution of fibers (used as a representative volume element, RVE), are simulated to analyze the impact of fiber–matrix interactions and their physical properties on the effective material behavior of fiber-reinforced HPC. In the final step, macroscopic boundary value problems (BVPs) based on a cuboid are simulated on a single scale using the developed phenomenological material model. The resulting macroscopic stress–strain characteristics obtained from both types of simulations, namely simulations of virtual experiments and macroscopic BVPs, are compared. This comparison is utilized for the calibration of material parameters to obtain a regularized solution and to assess the effectiveness of the presented phenomenological material model. [ABSTRACT FROM AUTHOR]

The GaoFen (GF)-5-02 satellite is one of the new generations of hyperspectral observation satellites launched by China in 2021. The directional polarimetric camera (DPC) is an optical sensor onboard the GF-5-02 satellite. The precipitable water vapor (PWV) is a key detection parameter of DPC. However, the existing PWV data developed using DPC data have significant errors due to the lack of the timely calibration of the two bands (865, 910 nm) of DPC used for PWV retrieval. In order to acquire DPC PWV data with smaller errors, a calibration method is developed for these two bands. The method consists of two parts: (1) calibrate the 865 nm band of the DPC using the cross-calibration method, (2) calibrate the 910 nm band of the DPC according to the calibrated 865 nm band of the DPC. This method effectively addresses the issue of the absence of a calibration method for the water vapor absorption band (910 nm) of the DPC. Regardless of whether AERONET PWV data or SuomiNET PWV data are used as the reference data, the accuracy of the DPC PWV data developed using calibrated DPC data is significantly superior to that of the DPC PWV data retrieved using data before recalibration. This means that the calibration method for the NIR bands of the DPC can effectively enhance the quality of DPC PWV data. [ABSTRACT FROM AUTHOR]

In this paper, a matching method for altimeter and transponder signals in Sub-optimal Maximum Likelihood Estimate (SMLE) tracking mode is proposed. In the in-orbit calibration of the altimeter in SMLE tracking mode using the reconstructive transponder, it is necessary to separate the forwarding signal from the ground echo signal. At the same time, the fluctuations in the received signal of the altimeter, which are caused by the forwarding signal of the transponder, can be eliminated. The transponder generates a bias when measuring the arrival time of the transmitting signal from the altimeter and embeds this bias in both the transponder-recorded data and the altimeter-recorded data. Therefore, the two sets of data have one-to-one correspondence, and they are superimposed using the sliding sum method. Moreover, the distance between the altimeter and the transponder is a parabolic geometric relationship, and the outliers are eliminated by the fitting error minimization decision, and the transponder signal is separated from the ground echo. The final altimeter transmitting–receiving signal path is obtained. Furthermore, the principles underlying this method can be used for any transponder that can adjust the response signal delay during calibration. [ABSTRACT FROM AUTHOR]

High-resolution melting (HRM) is a cost-efficient tool for targeted DNA methylation analysis. HRM yields the average methylation status across all CpGs in PCR products. Moreover, it provides information on the methylation pattern, e.g., the occurrence of monoallelic methylation. HRM assays have to be calibrated by analyzing DNA methylation standards of known methylation status and mixtures thereof. In general, DNA methylation levels determined by the classical calibration approach, including the whole temperature range in between normalization intervals, are in good agreement with the mean of the DNA methylation status of individual CpGs determined by pyrosequencing (PSQ), the gold standard of targeted DNA methylation analysis. However, the classical calibration approach leads to highly inaccurate results for samples with heterogeneous DNA methylation since they result in more complex melt curves, differing in their shape compared to those of DNA standards and mixtures thereof. Here, we present a novel calibration approach, i.e., temperature-wise calibration. By temperature-wise calibration, methylation profiles over temperature are obtained, which help in finding the optimal calibration range and thus increase the accuracy of HRM data, particularly for heterogeneous DNA methylation. For explaining the principle and demonstrating the potential of the novel calibration approach, we selected the promoter and two enhancers of MGMT, a gene encoding the repair protein MGMT. [ABSTRACT FROM AUTHOR]

This study proposes a model for the measurement of microscale liquid film thickness distribution using fluorescence signals. The interfacial conditions between the tool and the workpiece in mechanical machining are important for understanding these phenomena and mechanisms. In this study, indentation tests with transparent tools were used to observe interfaces; however, it was challenging to obtain the signal from a thin fluorescent liquid film on smooth and steeply inclined surfaces. Therefore, fluorescence-based measurement, such as laser-induced fluorescence, was employed. To measure the absolute thickness of the thin fluorescent film, calibration of the measurement system is necessary. Therefore, a theoretical model was proposed considering the multiple reflections of excitation light and fluorescence at the inclined surface between the indenter and workpiece. By measuring the profile of the surface topography of the indented workpiece and comparing the results with those measured by a surface profiler, the validity of the proposed calibration method and the performance of this measurement system were demonstrated. The measured surface profiles, including scratches of 2–4 µm, were in good agreement, demonstrating the validity of the proposed method. Highlights: Development of a model for measurement of liquid film thickness distribution at a steep, smooth indentation interface. Confirmation of the linearity between the fluorescence intensity and thickness, excitation intensity, and coefficient characterized by reflectance. Consistency of the measured film thickness distribution with the workpiece surface profile. [ABSTRACT FROM AUTHOR]

Orthotic devices may be prescribed for the management of foot and ankle deformities caused by Charcot-Marie-Tooth disease (CMT). However, the actual use of these devices is variable. No studies have evaluated the impact of prescription, delivery and follow-up of orthotic devices on their use. We aimed to describe the relationship between the pathways followed by individuals with CMT and orthotic device use. Exploratory, cross-sectional, 35-item survey of orthotic device management. Individuals with CMT were recruited from CMT-France Association. Of the 940 respondents, 795 were included, mean age of 52.9 (SD 16.9) years. Rate of orthotic device use was 49.2% (391/795). The most frequent reason for non-use was a poor fit. Non-use was related to the orthotic device type, the health professionals consulted, and the severity of the CMT-related impairments. Follow-up visits (38.7%), re-evaluation of orthotic devices (25.3%) and consultations with the Physical and Rehabilitation Medicine physician were infrequent (28.3%). Orthotic devices are massively underused. Follow-up and re-evaluation are infrequent. Care pathways, prescription and delivery of orthotic devices must be optimized to meet the expectations of people with CMT. Device fitting, individual needs, and changes in the clinical state must be re-evaluated regularly by specialists to improve orthotic device use. Foot deformities and foot drop contribute to disability in people with Charcot Marie Tooth Disease but use of orthotic devices is poor. To improve orthotic device use, regular multidisciplinary consultations and regular re-evaluation of the device by specialists in neuro-orthopaedics are important. The device fit should be regularly re-evaluated by the prescribing doctor or the practitioner who made the device. Regular multimodal evaluation of foot deformity, including muscle strength and length, and the individual's needs and expectations is also important to improve orthotic device use. [ABSTRACT FROM AUTHOR]

Summary Where the response variable in a big dataset is consistent with the variable of interest for small area estimation, the big data by itself can provide the estimates for small areas. These estimates are often subject to the coverage and measurement error bias inherited from the big data. However, if a probability survey of the same variable of interest is available, the survey data can be used as a training dataset to develop an algorithm to impute for the data missed by the big data and adjust for measurement errors. In this paper, we outline a methodology for such imputations based on an k‐nearest neighbours (kNN) algorithm calibrated to an asymptotically design‐unbiased estimate of the national total, and illustrate the use of a training dataset to estimate the imputation bias and the “fixed‐k asymptotic” bootstrap to estimate the variance of the small area hybrid estimator. We illustrate the methodology of this paper using a public‐use dataset and use it to compare the accuracy and precision of our hybrid estimator with the Fay–Harriot (FH) estimator. Finally, we also examine numerically the accuracy and precision of the FH estimator when the auxiliary variables used in the linking models are subject to undercoverage errors. [ABSTRACT FROM AUTHOR]

In this paper, a novel methodology is presented to evaluate the true nonlinear shear response of continuous fiber-reinforced plastic (CFRP) unidirectional laminae. It requires simple off-axis tensile experiments to be conducted and a finite element (FE) representation of them with a material constitutive law capable of handling nonlinearity in shear. Upon successful evaluation of the normal stiffness properties, the shear stress-strain response is derived via numerical calibration of the underlying FE models. The presented approach is also demonstrated by processing the raw data of an extensive material characterization test campaign conducted on a thermoplastic matrix CFRP. The outcome is compared to the conventional methods for shear response derivation using relevant standards such as ASTM D3518 and ASTM D5379. It has been successfully demonstrated that the pseudo-hardening phenomenon obtained from standard shear experiments as a result of fibers aligning with load direction can be eliminated with off-axis specimen tension experiments, thus, the true shear stress versus deformation response can be extracted up to failure. The main purpose of current work is to demonstrate the inconsistency in the available standard methods related to mechanical testing-based derivation of nonlinear in-plane shear behavior of UD plies. In addition, a novel technique is presented to achieve a more accurate prediction of nonlinear shear stress and strain along the entire representative loading range that contributes to more accurate simulations of composite parts up to failure and thus, better strength predictions. [ABSTRACT FROM AUTHOR]

Radar imaging SATellite (RISAT-1A), also known as Earth observation satellite-04 (EOS-04), is a follow-on mission of India’s first indigenously developed spaceborne C-band synthetic aperture radar (SAR) on-board RISAT-1 satellite. This article provides a description of the post-launch calibration and data quality evaluation of EOS-04 launched on 14 February 2022. Calibration devices (corner reflectors) of different shapes and sizes were deployed at Ahmedabad and Amrapur calibration sites in Gujarat, India from 2 to 24 April 2022, and their response in the EOS-04 data was used to assess the radiometric and polarimetric calibration. The results of the analysis showed satisfactory radiometric and polarimetric data quality. Geolocation accuracy was assessed using the ground-surveyed position of the corner reflectors and was found to be in accordance with the specified values of less than 50 m. [ABSTRACT FROM AUTHOR]

AbstractProbability predictions are essential to inform decision making across many fields. Ideally, probability predictions are (i) well calibrated, (ii) accurate, and (iii) bold, that is, spread out enough to be informative for decision making. However, there is a fundamental tension between calibration and boldness, since calibration metrics can be high when predictions are overly cautious, that is, non-bold. The purpose of this work is to develop a Bayesian model selection-based approach to assess calibration, and a strategy for boldness-recalibration that enables practitioners to responsibly embolden predictions subject to their required level of calibration. Specifically, we allow the user to pre-specify their desired posterior probability of calibration, then maximally embolden predictions subject to this constraint. We demonstrate the method with a case study on hockey home team win probabilities and then verify the performance of our procedures via simulation. We find that very slight relaxation of calibration probability (e.g., from 0.99 to 0.95) can often substantially embolden predictions when they are well calibrated and accurate (e.g., widening hockey predictions’ range from 26%–78% to 10%–91%). [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusions There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation.We studied pseudo‐values based on the Aalen‐Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR‐IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR‐IPCW). The MLR‐IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records.The pseudo‐value, BLR‐IPCW, and MLR‐IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low‐density regions of predicted transition probability.We recommend implementing either the pseudo‐value or BLR‐IPCW approaches to produce a calibration curve, combined with the MLR‐IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the “calibmsm” R package available on CRAN. [ABSTRACT FROM AUTHOR]

The tip of the red giant branch (TRGB) based distance method in the I band is one of the most efficient and precise techniques for measuring distances to nearby galaxies (D ≲ 15 Mpc). The TRGB in the near-infrared (NIR) is 1–2 mag brighter relative to the I band, and has the potential to expand the range over which distance measurements to nearby galaxies are feasible. Using Hubble Space Telescope (HST) imaging of 12 fields in eight nearby galaxies, we determine color-based corrections and zero-points of the TRGB in the Wide Field Camera 3 IR (WFC3/IR) F110W and F160W filters. First, we measure TRGB distances in the I band equivalent Advanced Camera System (ACS) F814W filter from resolved stellar populations with the HST. The TRGB in the ACS F814W filter is used for our distance anchor and to place the WFC3/IR magnitudes on an absolute scale. We then determine the color dependence (a proxy for metallicity/age) and zero-point of the NIR TRGB from photometry of WFC3/IR fields that overlap with the ACS fields. The new calibration is accurate to ∼1% in distance relative to the F814W TRGB. Validating the accuracy of the calibrations, we find that the distance modulus for each field using the NIR TRGB calibration agrees with the distance modulus of the same field as determined from the F814W TRGB. This is a JWST preparatory program, and the work done here will directly inform our approach to calibrating the TRGB in JWST NIRCam and NIRISS photometric filters. [ABSTRACT FROM AUTHOR]

Fertilizer recommendations for phosphorus (P) are generally based on the soil content of plant available P and the amount of P exported by the harvested crop. In countries of the former Soviet Union (SU), a widespread method is the Machigin extraction. The relationships between soil P contents extracted with this and other European methods are not well established. Here, we compare the Machigin soil P extraction method with the Calcium Ammonium Lactate P(CAL) procedure that is commonly used in Central Europe. Both methods were tested with 94 soil samples originating from Kyrgyzstan, Ukraine, and Germany. Overall, the P amount extracted with the CAL method was 2.8 times higher than that extracted with the Machigin method. However, using results from all analyzed individual soil samples, the relation between P(CAL) and P(Machigin) showed large scatter and an R2 of only 0.364. Differing relations between P(CAL) and P(Machigin) were observed for soils with pH >7 and <7. However, these are overshadowed by differences between the three geographic groups of the soil samples. The estimated soil P fertility classes for the Machigin extraction were mostly higher than for the German (CAL) procedure, resulting in lower fertilizer recommendations for the Machigin method. To improve the accuracy of conversion between both methods, evaluation of laboratory proficiency tests (i.e. analysis of the same soil samples in different laboratories with the same methods) of the Machigin method should be useful. [ABSTRACT FROM AUTHOR]

Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of 1 week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats. [ABSTRACT FROM AUTHOR]

The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose. [ABSTRACT FROM AUTHOR]

Ransomware poses a significant threat by encrypting files or systems demanding a ransom be paid. Early detection is essential to mitigate its impact. This paper presents an Uncertainty-Aware Dynamic Early Stopping (UA-DES) technique for optimizing Deep Belief Networks (DBNs) in ransomware detection. UA-DES leverages Bayesian methods, dropout techniques, and an active learning framework to dynamically adjust the number of epochs during the training of the detection model, preventing overfitting while enhancing model accuracy and reliability. Our solution takes a set of Application Programming Interfaces (APIs), representing ransomware behavior as input we call "UA-DES-DBN". The method incorporates uncertainty and calibration quality measures, optimizing the training process for better more accurate ransomware detection. Experiments demonstrate the effectiveness of UA-DES-DBN compared to more conventional models. The proposed model improved accuracy from 94% to 98% across various input sizes, surpassing other models. UA-DES-DBN also decreased the false positive rate from 0.18 to 0.10, making it more useful in real-world cybersecurity applications. [ABSTRACT FROM AUTHOR]

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions. [ABSTRACT FROM AUTHOR]

The traditional stratification weight is widely used in survey sampling for estimation under stratified random sampling (StRS). A neutrosophic calibration approach is proposed under neutrosophic statistics for the first time with the aim of improving conventional stratification weight. This addresses the challenge of estimating the empirical cumulative distribution function (CDF) of a finite population using the neutrosophic technique. The neutrosophic technique extends traditional statistics, dealing with indeterminate, vague, and uncertain values. Thus, using additional information, we are able to obtain an effective estimate of the neutrosophic CDF. The suggested estimator yields an interval range in which the population empirical CDF is likely to exist rather than a single numerical value. The proposed family of neutrosophic estimators will be defined under suitable calibration constraints. A simulation study is also computed in order to assess the effectiveness of the suggested and adapted neutrosophic estimators using real-life symmetric and asymmetric datasets. [ABSTRACT FROM AUTHOR]

Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model's accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts. [ABSTRACT FROM AUTHOR]

During the construction of immersed tube tunnels, the immersed tube is in a floating state after the installation of the measurement tower, and it is difficult to acquire the position of the installed positioning equipment on the measuring tower and the geometric relationship with the immersed tube due to its swaying state. A "back -calibration" measurement method is proposed, which includes the calibration of the measurement and control system and the correction of positioning accuracy. The calibration of the measurement and control system encompasses the initial calibration of the tube section and the measuring tower. The correction of positioning accuracy includes establishing the construction coordinate system, data processing, accuracy comparison, and correction. By installing a GNSS receiver on the top feature points of the tube to collect its coordinates in real-time and compare them with the real-time calculation results of the measurement tower's control system, an analysis of the root mean square error before and after back-calibration is conducted through engineering cases. The significant reduction in root mean square error after back-calibration proves the effectiveness of the back-calibration method in improving measurement accuracy, thereby ensuring the accuracy and reliability of the immersed tube installation process. [ABSTRACT FROM AUTHOR]

Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins. [ABSTRACT FROM AUTHOR]

Calibration of titration calorimeters is an ongoing problem, particularly with calorimeters with reaction vessel volumes < 10 mL in which an electrical calibration heater is positioned outside the calorimetric vessel. Consequently, a chemical reaction with a known enthalpy change must be used to accurately calibrate these calorimeters. This work proposes the use of standard solutions of potassium acid phthalate (KHP) titrated into solutions of excess sodium hydroxide (NaOH) or excess tris(hydroxymethyl)aminomethane (TRIS) as standard reactions to determine the collective accuracy of the relevant variables in a determination of the molar enthalpy change for a reaction. KHP is readily available in high purity, weighable for easy preparation of solutions with accurately known concentrations, stable in solution, not compromised by side reactions with common contaminants such as atmospheric CO2, and non-corrosive to materials used in calorimeter construction. Molar enthalpy changes for these reactions were calculated from 0 to 60 °C from reliable literature data for the pKa of KHP, the molar enthalpy change for protonation of TRIS, and the molar enthalpy change for ionization of water. The feasibility of using these reactions as enthalpic standards was tested in several calorimeters; a 50 mL CSC 4300, a 185 μL NanoITC, a 1.4 mL VP-ITC, and a TAM III with 1 mL reaction vessels. The results from the 50 mL CSC 4300, which was accurately calibrated with an electric heater, verified the accuracy of the calculated standard values for the molar enthalpy changes of the proposed reactions. [ABSTRACT FROM AUTHOR]