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Recently, UWB-based technology providing centimeter-level accuracy has been developed and widely utilized in indoor real-time location tracking systems. However, location accuracy varies due to factors such as frequency interference, collisions, reflected signals, and whether line-of-sight (LOS) conditions are met, and it can be challenging to ensure high accuracy in specific environments. Fortunately, when anchor positions are fixed, the locations of large obstacles such as columns or furniture remain relatively stable, leading to similar patterns of positioning bias at specific points. This study proposes an algorithm that corrects inaccurate positioning to more closely reflect the actual location based on bias and deviation maps generated using natural neighbor interpolation. Initially, positioning bias and deviations at specific points are sampled, and bias and deviation maps are created using natural neighbor interpolation. During location tracking, the algorithm detects candidate clusters and determines the centroid to estimate the actual location by applying the bias and deviation maps to the measured positions derived through trilateration. To validate the proposed algorithm, experiments were conducted in a non-LOS (NLOS) indoor environment. The results demonstrate that the proposed algorithm can reduce the positioning bias of a UWB-based RTLS by approximately 71.34% compared to an uncalibrated system. [ABSTRACT FROM AUTHOR]

Hydrodynamic models (HMs) are tools for simulating flow behavior through the solution of conservation equations. These equations can have different degrees of simplification, which influence the model structure. One-dimensional (1D) HMs are still popular due to their simplicity. A crucial parameter for obtaining accurate 1D HM outputs is the effective Manning roughness factor (EMRF). The EMRF reflects additional numerical and dissipative aspects beyond boundary roughness. Although generalized likelihood uncertainty estimation (GLUE) is an important method for uncertainty analysis, it requires the selection of a likelihood function and a cutoff threshold. The goal of this study was to determine the effect of the likelihood function on the EMRF characteristics for mountain river morphologies, considering a certain cutoff threshold. The results show that the error model and the treatment of the residual in the objective function affect the EMRF range and limits in the studied reaches with a cascade or step pool. Furthermore, the analysis shows that these morphologies deviate from the model structure, which may affect the likelihood curve shape. Notably, the EMRF and measured roughness did not intersect in the studied reach with a plane bed, which is attributed to the presence of vegetation on the banks of that reach. [ABSTRACT FROM AUTHOR]

The full life-cycle state monitoring of bridge structures is an effective way to ensure traffic safety and is also an important trend in the development of modern transportation. The accuracy, traceability, and reliability of sensor data are the foundation for the Bridge Health Monitoring (BHM) system to achieve its various functions. However, commonly seen uncertainties in measurement results of the monitoring system such as error, linearity, and repeatability often need to be calibrated to ensure accuracy and reliability of the data. Therefore, the calibration of these basic uncertain elements has been brought to our research focus. In this study, we first comb the monitoring parameters and characteristics of different sensor systems to help select suitable bridge structure monitoring sensors and adopt appropriate calibration and traceability strategies. Then, in combination with the research on traditional sensor calibration techniques and new sensor calibration technology, we present the key factors to be considered in the sensor calibration process and the challenges faced by the current technologies. Finally, suggestions are made for the research trend on the calibration of bridge monitoring sensors, aiming to provide reference for both theoretical and practical studies on bridge sensor calibration in the future. [ABSTRACT FROM AUTHOR]

With the progress of micro electromechanical system (MEMS) technology, the performance of MEMS inertial measurement unit (IMU) composed of gyroscopes and accelerometers has been improved. Among the inertial sensors, MEMS triaxial gyroscope plays an important role in attitude estimation, navigation and positioning of intelligent mobile terminals such as unmanned aerial vehicles and unmanned vehicles. However, the measured values of low and medium cost MEMS triaxial gyroscopes are mainly affected by temperature (or thermal effect) and random errors. As results, the drift errors correlated with temperature will reduce application accuracy of MEMS triaxial gyroscope. The traditional calibration method for thermal drift errors relies on the expensive equipment, such as turntable and the temperature control system, which increases the cost of calibration. Therefore, an effective thermal calibration method that is available when using low- or high-cost tools for MEMS triaxial gyroscope will be meaningful for majority users. Hence, this paper analyzed and established the thermal drift model of MEMS triaxial gyroscope, and proposed a segmented systematic calibration method based on 24 states according to this model. Simulation shows that the proposed method can obtain the results approaching the real parameter thermal drift curves. Calibration experiments and parameters test show that the max errors of attitude calculation are reduced to 10% of the results using the original parameters, which indicates that the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background: The use of non-invasive risk scores to detect undiagnosed type 2 diabetes (T2D) ensures the restriction of invasive and costly blood tests to those most likely to be diagnosed with the disease. This study assessed and compared the performance of the African Diabetes Risk Score (ADRS) with three other diabetes risk prediction models for identifying screen-detected diabetes based on fasting plasma glucose (FPG) or glycated haemoglobin (HBA1c). Methods: Age, sex, waist circumference, body mass index, blood pressure, history of diabetes and physical activity levels from the SA-NW-PURE study were used to externally validate the ADRS and other established risk prediction models. Discrimination was assessed and compared using C-statistics and nonparametric methods. Calibration was assessed using calibration plots, before and after recalibration. Results: Nine hundred and thirty-seven participants were included; 14% had prevalent undiagnosed T2D according to FPG and 26% according to HbA1c. Discrimination was acceptable and was mostly similar between models for both diagnostic measures. The C-statistics for diagnosis by FPG ranged from 0.69 for the Simplified FINDRISC model to 0.77 for the ADRS model and 0.77 for the Simplified FINDRISC model to 0.79 for the ADRS model for diagnosis by HbA1c. Calibration ranged from acceptable to good, though over- and underestimation were present. All models improved significantly following recalibration. Conclusions: The models performed comparably, with the ADRS offering a non-invasive way to identify up to 79% of cases. Based on its ease of use and performance, the ADRS is recommended for screening for T2D in certain Black population groups in South Africa. HbA1c as a means of diagnosis also showed comparable performance with FPG. Therefore, further validation studies can potentially use HbA1c as the standard to compare to. [ABSTRACT FROM AUTHOR]

Quantum error mitigation (QEM) is vital for improving quantum algorithms' accuracy on noisy near-term devices. A typical QEM method, called Virtual Distillation (VD), can suffer from imperfect implementation, potentially leading to worse outcomes than without mitigation. To address this, we introduce Circuit-Noise-Resilient Virtual Distillation (CNR-VD), which includes a calibration process using simple input states to enhance VD's performance despite circuit noise, aiming to recover the results of an ideally conducted VD circuit. Simulations show that CNR-VD significantly mitigates noise-induced errors in VD circuits, boosting accuracy by up to tenfold over standard VD. It provides positive error mitigation even under high noise, where standard VD fails. Furthermore, our estimator's versatility extends its utility beyond VD, enhancing outcomes in general Hadamard-Test circuits. The proposed CNR-VD significantly enhances the noise-resilience of VD, and thus is anticipated to elevate the performance of quantum algorithm implementations on near-term quantum devices. This study focuses on reducing noise in the circuit used in the quantum error mitigation method called Virtual Distillation (VD). The authors introduce an approach which significantly enhances the noise tolerance of VD and improves the accuracy by an order of magnitude compared to the standard VD. [ABSTRACT FROM AUTHOR]

Models are simplified descriptions of reality and are intrinsically limited by the assumptions that have been introduced in their formulation. With the development of automatic calibration toolboxes, finding optimal parameters that suit the environmental system has become more convenient. Here, we explore how optimization toolboxes can be applied innovatively to uncover flaws in the physical formulations of models. We illustrate this approach by evaluating the effect of simplifications embedded in the formulation of a widely used hydro-thermodynamic model. We calibrate a Delft3D model based on temperature profiles for a case study, Lake Morat (Switzerland), through the optimization tool DYNO-PODS. Results show that neglecting the fraction β of shortwave radiation absorbed at the water surface can be compensated by higher values of the light extinction coefficient. This leads to unrealistic values of the latter parameter, as the optimization pushes the coefficient towards the limit of no transparency, consistent with the need to reproduce a significant absorption at the surface. While it is well-known that β is significantly larger than zero, its absence in the model was never noticed as critical. The extensive use of automatic calibration tools may offer similar outcomes in other applications. [ABSTRACT FROM AUTHOR]

We discovered that employing inappropriate calibration curves for activity evaluation resulted in false positive results. Specifically, an artificial efficiency of hydrogen production is exaggerated by up to 2.2-fold if the calibration curves are misused, leading to considerably high false positive results. Our study highlights the importance of utilizing the correct calibration curve to ensure a true performance, and is beneficial for fostering advancements in the development of thermal-assisted photocatalysis. [ABSTRACT FROM AUTHOR]

Background: Neuroinflammation is widely recognized as a key factor in the pathogenesis of Alzheimer's disease (AD), alongside ß-amyloid deposition and the formation of neurofibrillary tangles. The NLR family pyrin domain containing 3 (NLRP3) inflammasome, part of the innate immune system, has been implicated in the neuropathology of both preclinical amyloid and tau transgenic models. Activation of the NLRP3 pathway involves an initial priming step, which increases the expression of Nlrp3 and interleukin (IL)-1ß, followed by the assembly of the NLRP3 inflammasome complex, comprising NLRP3, ASC, and caspase-1. This assembly leads to the proteolytic maturation of the pro-inflammatory cytokines IL-1ß and IL-18. Additionally, the NLRP3 inflammasome induces Gasdermin D (GSDMD) cleavage, forming membrane pores through which IL-1ß and IL-18 are secreted. Inhibition of NLRP3 has been shown to enhance plaque clearance by modulating microglial activation. Furthermore, blocking NLRP3 in tau transgenic mice has been found to reduce tau phosphorylation by affecting the activity of certain tau kinases and phosphatases. Methods: In this study, organotypic brain slice cultures from P301S transgenic mice were treated with lipopolysaccharide (LPS) plus nigericin as a positive control or exposed to tau seeds (K18) to evaluate NLRP3 inflammasome activation. The effect of tau seeding on NLRP3 activity was further examined using Meso Scale Discovery (MSD) assays to measure IL1ß secretion levels in the presence and absence of NLRP3 inhibitors. The role of NLRP3 activity was investigated in fullbody Nlrp3 knockout mice crossbred with the tau transgenic P301S model. Additionally, full-body and microglia-selective Gsdmd knockout mice were crossbred with P301S mice, and tau pathology and neurodegeneration were evaluated at early and late stages of the disease using immunohistochemistry and biochemical assays. Results: Activation of the NLRP3 pathway was observed in the mouse organotypic slice culture (OSC) model following stimulation with LPS and nigericin or exposure to tau seeds. However, Nlrp3 deficiency did not mitigate tauopathy or neurodegeneration in P301S mice in vivo, showing only a minor effect on plasma neurofilament (NF-L) levels. Consistently, Gsdmd deficiency did not alter tau pathology in P301S mice. Furthermore, neither full-body nor microglia-selective Gsdmd deletion had an impact on neuronal pathology or the release of pro-inflammatory cytokines. Conclusion: The absence of key components of the NLRP3 inflammasome pathway did not yield a beneficial effect on tau pathology or neurodegeneration in the preclinical Tau-P301S mouse model of AD. Nonetheless, organotypic slice cultures could serve as a valuable ex vivo mechanistic model for evaluating NLRP3 pathway activation and pharmacological inhibitors. [ABSTRACT FROM AUTHOR]

Background: The development of readily available wearable accelerometers has enabled clinicians to objectively monitor physical activity (PA) remotely in the community, a superior alternative to patient self-reporting measures. Critical to the value of these monitors is the ability to reliably detect when patients are undergoing ambulatory activity. Previous studies have highlighted the strength of using mean amplitude deviation (MAD) as a universal measure for analysing raw accelerometery data and defining cut-points between sedentary and ambulatory activities. Currently however there is little evidence surrounding the use of chest-worn accelerometers which can provide simultaneous monitoring of other physiological parameters such as heart rate (HR), RR intervals, and Respiratory Rate alongside accelerometery data. We aimed to calibrate the accelerometery function within the VivaLink ECG patch to determine the cut-point MAD value for differentiating sedentary and ambulatory activities. Methods: We recruited healthy volunteers to undergo a randomised series of 9 activities that simulate typical free-living behaviours, while wearing a VivaLink ECG Patch (Campbell, California). MAD values were applied to a Generalised Linear Mixed Model to determine cut-points between sedentary and ambulatory activities. We constructed a Receiver Operating Characteristic (ROC) curve to analyse the sensitivity and specificity of the cut-off MAD value. Results: Eighteen healthy adults volunteered to the study and mean MAD values were collected for each activity. The optimal MAD cut-point between sedentary and ambulatory activities was 47.73mG. ROC curve analysis revealed an area under the curve of 0.99 (p < 0.001) for this value with a sensitivity and specificity of 98% and 100% respectively. Conclusion: In conclusion, the MAD cut-point determined in our study is very effective at categorising sedentary and ambulatory activities among healthy adults and may be of use in monitoring PA in the community with minimal burden. It will also be useful for future studies aiming to simultaneously monitor PA with other physiological parameters via chest worn accelerometers. [ABSTRACT FROM AUTHOR]

Background: The intricate task of diagnosing and managing small renal masses (SRMs) has become progressively convoluted within the realm of clinical practice. Contemporary clinical prediction instruments may succumb to a gradual decay in precision, coupled with an absence of unambiguous guidelines to navigate patient management. Methods: This investigation was devised to formulate and authenticate nomograms for the overall survival (OS) and cancer- specific survival (CSS) among patients afflicted with SRMs. The study encompassed a cohort of 2558 pediatric patients diagnosed with SRMs over the period of 2000 to 2019. Independent prognostic indicators for OS and CSS, encompassing historical staging, chemotherapy regimens, surgical interventions, and pathological classifications, were ascertained through the employment of multivariate Cox proportional hazards regression analysis and backward stepwise selection. Results: Through the utilization of multivariate Cox regression models, nomograms for OS and CSS were meticulously crafted, demonstrating commendable discrimination and calibration within the training set (OS C-index: 0.762, CSS C-index: 0.779). The validation set further corroborated the exemplary discrimination and calibration of the nomograms. Moreover, these nomograms adeptly differentiated between patient groups at elevated and diminished risk levels. Conclusion: The nomograms delineated in this research provide propitious predictive accuracy for overall survival and cancer-specific survival in patients suffering from pediatric SRMs, thereby contributing to refined risk stratification and steering the optimal therapeutic course of action. The necessity for supplementary validation prevails before the translation of these findings into clinical practice. [ABSTRACT FROM AUTHOR]

We report a new image-based inter-analyst study to investigate fission-track grain selection and analysis by 13 participants from an image data set that included grains of variable quality. Results suggest that participants with less experience show a higher rate of selecting unsuitable grains, while participants from the same laboratories generally provide similar results. Less analysis experience may result in the rejection of suitable grains, or inclusion of unsuitable ones. While inappropriate omission and inclusion can both bias results, the latter is more pernicious due to the standard practice of achieving a predecided number of analyses; particularly in difficult samples, there is a danger of "squeezing the rock" by weakening selection criteria. Juxtaposing selected regions of interest (ROIs) on the same grains indicates that zoned grains and grains with inclusions and defects yield varying track density estimates, indicating that ROI placement can be an influential factor. We propose developing image data repositories for global data transparency, a global guidance for fission-track analysis, digital teaching modules, and open science. We also point out the need for new approaches for zeta calibration that include consideration of grain quality, methods of uranium determination, and etching protocols. [ABSTRACT FROM AUTHOR]

Despite their use in ophthalmology for more than a century, there are no fundamental biophysical models that account for the dissolution rate of intra-ocular gas bubbles in vivo, as a function of their initial size and composition. Any physical model that reliably predicts the evolution of these bubbles would be extremely useful for planning treatment, by predicting their size over time, and their periods of clinical usefulness. A fundamental, computational, biophysical model is developed here with these purposes in mind. The model is fundamental in the sense that the underlying rate equations are derived using linear response theory, wherein the driving force is a solute chemical potential difference. It is computational because the rate equations for the bubble volume and composition are fully coupled and must be integrated numerically and simultaneously. It was rendered clinically relevant by fitting its two adjustable parameters (two rate constants) to recently acquired live human eye data on intra-ocular air bubble dissolution rates. The model consists of an explicitly four-component, dome-shaped gas bubble, comprised of N2, O2, H2O and CO2, which exchanges N2 and O2 with circulating venous blood in the capillaries of the eye. The calibrated model provided an excellent fit to the available data, and interpolated nicely through three points not used for the calibration. It was used to show that by selecting different N2/O2 ratios in the initially injected gas, one can generate intra-ocular gas bubbles with significantly different predicted periods of clinical usefulness. This potentially useful predictive capability does not exist in current clinical practice. [ABSTRACT FROM AUTHOR]

Computational imaging, as a novel technology utilizing encoded image acquisition, relies on intelligent decoding methods for effective image restoration and sensing. Optical computing‐based decoders can efficiently process and extract features from pre‐sensor information, reducing the computational burden on digital computers. However, mainstream parallel optical neural network (ONN) architectures based on wavefront propagation typically possess complex network structures and high‐precision parameters, which pose challenges in terms of precise fabrication and system calibration, as well as sensitivity to signal‐to‐noise ratios. In this work, a binary‐weighted optical computing engine is proposed with spatial multiplexing and aggregation (B‐OSMA), a large‐scale passive ONN implementation that achieves high‐efficiency image sensing. Employing B‐OSMA as an optical decoder, demonstrated image categorizing from 2% compressive is experimented sampling with 92.0% and 83.8% accuracy on MNIST and fashion‐MNIST datasets, respectively, approaching the performance of full‐precision electronic computing while reducing storage requirements by 97%. Compared to conventional ONNs with analog weights, the B‐OSMA exhibits enhanced resilience against systematic errors and ambient noise. This work represents a significant advancement towards practical applications of optical computing in image sensing. [ABSTRACT FROM AUTHOR]

Developing simple and accurate hydrologic models for wet-weather sanitary sewer flow has long been enigmatic for the engineering community. Hydrologic models suitable for surface water hydrology often perform poorly when simulating sanitary sewer hydrology when they do not account for antecedent moisture conditions and seasonality. The result is underpredicting peak flow and volume in wet conditions and overpredicting the same in dry conditions. The Antecedent Moisture Model is an empirically-calibrated method which models a rainfall capture fraction that varies by antecedent moisture and season, and transforms the captured rainfall into a flow hydrograph. The Antecedent Moisture Model has been particularly successful in modeling sanitary sewer infiltration, for which other methods perform poorly. This paper suggests a reparameterization of the original model which provides virtually identical results while improving interpretability and ease of use. The reparametrized model is designed to be time step independent and more physically relatable, intuitive, and scaleable than the original parameterization while remaining functionally equivalent to the original parameterization. The paper also presents two computational examples which demonstrate the application of the Antecedent Moisture Model formulas and math. [ABSTRACT FROM AUTHOR]

In order to systematically obtain the excavation characteristic parameters for ginger harvesting, experimental analysis was conducted on the discrete elemental parameters in a particle simulation model of the ginger-soil system. Through stacking tests, the surface energy of soil-ginger tuber JKR was determined to be 3.7 J/m², the coefficient of static friction of soil-steel (65 Mn) was 0.56, the coefficient of rolling friction was 0.03, and the coefficient of restitution of collision was 0.40. Utilizing normal and lateral compression tests conducted on the soil body, the soil base parameters required for the Bonding model were determined. Subsequently, a three-dimensional model of ginger root and stem was constructed using these parameters. With the aid of 3D scanning technology, a discrete element parameter model was established for the ginger field during the harvesting period. On the basis of the measured parameters, a three-dimensional model of ginger rhizome was established and finally a discrete parameter model of ginger field was constructed in the harvesting period. The calibration parameters are highly reliable after the model's tightness and field harvesting test, which provides reliable data support for the soil flow and the force of the soil-touching parts during the later simulation of ginger harvesting and digging operation. [ABSTRACT FROM AUTHOR]

This study constructed a numerical model using the discrete element software EDEM to address the current lack of calibrated contact parameters for peanut seedling membranes and the absence of precise simulation model parameters for mechanized separation. The Hysteretic Spring Contact Model (HSCM) was employed to calibrate the contact parameters of peanut seedling membranes. The angle of repose of peanut seedling membranes was determined through image processing combined with the least squares method. Through central composite design (CCD), a second-order response model linking the contact parameters to the angle of repose was established. Optimization was achieved by using the angle of repose obtained from physical tests as the objective. Secondary simulation tests were conducted with the calibrated parameters, revealing a relative error of 1.37% between the simulated and physical angles of repose. This confirmed the effectiveness of the parameters in calibrating peanut seedling membrane characteristics. The findings offer theoretical and empirical support for discrete element simulations of peanut seedling membrane separation and peanut straw pulverization processes. [ABSTRACT FROM AUTHOR]

The Mt. Wilson Observatory archive of observations of solar disk magnetic fields, Doppler velocities, and spectral line intensities is a resource for studying the Sun's state from 1967 to 2013. Instrument changes/upgrades during this time must be considered when interpreting this record. Portions of this record have been previously released. This publication documents the data record in order to allow its independent interpretation. The archive is available through two directory trees which can be accessed at http://sha.stanford.edu/mwo/msm.html. The calibration of the observations is impacted by the solar surface convective flows, which produce offsets for both differential rotation and meridional circulation functions. The effects of these offsets have been reduced in this and other publications by temporal averaging. [ABSTRACT FROM AUTHOR]

Zonal calibration algorithm is the most widely used method to extend the measurement range of five-hole probes. However, large measurement error will be aroused near the boundary between two neighboring zones and this is acknowledged as the inner boundary measurement problem of zonal calibration algorithm. To tackle this problem, a two-dimensional uniform flow model is developed in this paper to describe the relationship between pressure from holes and flow angles. Based on this model, a method to adjust the boundary between two neighboring zones automatically with respect to inlet flow conditions is developed. With this novel method, the data extrapolation of zonal calibration algorithm at measurement stations near the boundary between two neighboring zones is avoided, and the corresponding large measurement error is eliminated. According to the experimental data, maximum measurement error of total pressure and flow angle can reach 7.5% and 3.2°, and will be reduced to 0.89% and 0.12° by the novel method. Resultantly, the inner boundary measurement problem of zonal calibration algorithm is solved. Influences of several key parameters on the measurement accuracy of the novel method are investigated too, and criteria to adjust the boundary between two neighboring zones are given. Conclusions of this paper can be used to further improve the accuracy of five-hole probes in measuring large angle flows. [ABSTRACT FROM AUTHOR]

BACKGROUND: Neoadjuvant chemoradiotherapy (nCRT) for rectal cancer can lead to structural changes in collagen in the tumor microenvironment and increase the risk of postoperative anastomotic stenosis (AS). However, the quantitative relationship between AS and collagen has not been defined. This study is to quantitatively analyze the collagen features in rectal cancer and explore the relationship between the changes of collagen and postoperative anastomotic stenosis after nCRT. STUDY DESIGN: This is a retrospective study. A total of 371 patients with rectal cancer were included. Collagen features in the resection margin of rectal cancer anastomosis was extracted by multiphoton imaging. The least absolute shrinkage operator logistic regression was performed to select features related to AS and the collagen score (CS) was constructed. Area under the receiver operating curve (AUROC) and decision curve analysis were performed to evaluate the discrimination and clinical benefit of the nomogram. RESULTS: The probability of AS was 23% in the training cohort and 15.9% in the validation cohort. In the training cohort, the distance between tumor and resection margin, anastomotic leakage and CS were independent risk factors for postoperative AS in univariate and multivariate analyses. A nomogram was constructed based on these results. The prediction nomogram showed good discrimination (AUROC 0.864; 95% CI 0.776 to 0.952) and was validated in the validation cohort (AUROC 0.918; 95% CI 0.851 to 0.985). CONCLUSIONS: CS is an independent risk factor for AS in rectal cancer after nCRT. The predictive model based on CS can predict the occurrence of postoperative AS. [ABSTRACT FROM AUTHOR]

Recovering the fine-grained working process of couriers is becoming one of the essential problems for improving the express delivery systems because knowing the detailed process of how couriers accomplish their daily work facilitates the analyzing, understanding, and optimizing of the working procedure. Although coarse-grained courier trajectories and waybill delivery time data can be collected, this problem is still challenging due to noisy data with spatio-temporal biases, lacking ground truth of couriers' fine-grained behaviors, and complex correlations between behaviors. Existing works typically focus on a single dimension of the process such as inferring the delivery time and can only yield results of low spatio-temporal resolution, which cannot address the problem well. To bridge the gap, we propose a digital-twin-based iterative calibration system (DTRec) for fine-grained courier working process recovery. We first propose a spatio-temporal bias correction algorithm, which systematically improves existing methods in correcting waybill addresses and trajectory stay points. Second, to model the complex correlations among behaviors and inherent physical constraints, we propose an agent-based model to build the digital twin of couriers. Third, to further improve recovery performance, we design a digital-twin-based iterative calibration framework, which leverages the inconsistency between the deduction results of the digital twin and the recovery results from real-world data to improve both the agent-based model and the recovery results. Experiments show that DTRec outperforms state-of-the-art baselines by 10.8% in terms of fine-grained accuracy on real-world datasets. The system is deployed in the industrial practices in JD Logistics with promising applications. The code is available at https://github.com/tsinghua-fib-lab/Courier-DTRec. [ABSTRACT FROM AUTHOR]

OBJECTIVES A study of the performance of in-hospital/30-day mortality risk prediction models using an alternative machine learning algorithm (XGBoost) in adults undergoing cardiac surgery. METHODS Retrospective analyses of prospectively routinely collected data on adult patients undergoing cardiac surgery in the UK from January 2012 to March 2019. Data were temporally split 70:30 into training and validation subsets. Independent mortality prediction models were created using sequential backward floating selection starting with 61 variables. Assessments of discrimination, calibration, and clinical utility of the resultant XGBoost model with 23 variables were then conducted. RESULTS A total of 224,318 adults underwent cardiac surgery during the study period with a 2.76% (N  = 6,100) mortality. In the testing cohort, there was good discrimination (area under the receiver operator curve 0.846, F1 0.277) and calibration (especially in high-risk patients). Decision curve analysis showed XGBoost-23 had a net benefit till a threshold probability of 60%. The most important variables were the type of operation, age, creatinine clearance, urgency of the procedure and the New York Heart Association score. CONCLUSIONS Feature-selected XGBoost showed good discrimination, calibration and clinical benefit when predicting mortality post-cardiac surgery. Prospective external validation of a XGBoost-derived model performance is warranted. [ABSTRACT FROM AUTHOR]

Acoustical parameter estimation is a routine task in many domains. The performance of existing estimation methods is affected by external uncertainty, yet the methods provide no measure of confidence in the estimates. Hence, it is crucial to quantify estimate uncertainty before real-world deployment. Conformal prediction (CP) generates statistically valid prediction intervals for any estimation model using calibration data; a limitation is that calibration data needed by CP must come from the same distribution as the test-time data. In this work, we propose to use CP to obtain statistically valid uncertainty intervals for acoustical parameter estimation using a data-driven model or an analytical model without training data. We consider direction-of-arrival estimation and localization of sources. The performance is validated on plane wave data with different sources of uncertainty, including ambient noise, interference, and sensor location uncertainty. The application of CP for data-driven and traditional propagation models is demonstrated. Results show that CP can be used for statistically valid uncertainty quantification with proper calibration data. [ABSTRACT FROM AUTHOR]

MAJIS (Moons and Jupiter Imaging Spectrometer) is the imaging spectrometer onboard ESA's JUICE (JUpiter and ICy Moons Explorer) spacecraft that operates in the visible and near/mid-infrared between 0.5 and 5.54 μm. Before the launch of JUICE in April 2023, MAJIS underwent a comprehensive on-ground calibration campaign in between August and September 2021 in the IAS (Institut d'Astrophysique Spatiale, Université Paris-Saclay) calibration facilities. Among all the operations, calibration sequences using a set of natural mineral samples and synthetic reference materials were acquired in order to characterize MAJIS performances under conditions assumed to be close to certain future observation configurations. Here, we analyze these calibration measurements using comparison with laboratory reference spectra to quantify MAJIS spectral and spatial performances while observing these solid surfaces. We first assess the MAJIS absolute spectral calibration of the visible and near-infrared channel covering half of the wavelength range. We then quantify spectral performances in terms of global spectral slopes, band detection, band shape, and depth retrievals, over most of the spectral range using six mineral samples. We conclude that for most configurations, the MAJIS instrument demonstrates excellent spectral performances compliant with the requirements. MAJIS can, however, be affected by stray light contributions, notably for wavelengths lower than about 1.2 μm, and some performances of the instrument may then be significantly impacted depending on viewing conditions. In particular, we have identified cases of spectral contrast reduction up to 40%, absolute spectral shifts up to 2–3 nm, and spectral smile variability by +/1 nm. Finally, we used the MAJIS internal scanning mirror to test its ability to construct hyperspectral images of a few samples: we present the first band depth maps derived with MAJIS while observing a serpentine/carbonate sample, as well as an evaluation of MAJIS spatial point spread function. Overall, the analysis of MAJIS behavior while observing samples confirms most MAJIS expected performance requirements, while revealing subtle spectral perturbations that may be related to stray light and viewing conditions. These differences will be further investigated in-flight during the cruise, with a solar reflected target such as the Moon, as well as Jupiter before the JUICE orbital insertion. [ABSTRACT FROM AUTHOR]

Non-dispersive infrared (NDIR) gas detection systems have been extensively used for gas monitoring because they are considered the simplest approach with moderate sensitivity and fast response. Concentration calculation models for calibration are particularly important in NDIR gas detection systems because of the nonlinear relationship between the output voltage ratio and concentration. We propose a concentration calculation model with two fitting parameters for non-dispersive infrared gas detection systems, accounting for the fact that not all IR radiation that impinges upon the detector is absorbed by the gas and for the saturation effects in absorption peaks leading to the nonlinear relationship. Despite the small number of fitting parameters of the proposed concentration calculation model, it presents high quality curve fitting to experimental data. The performance of the proposed model is compared to that of other concentration calculation models. [ABSTRACT FROM AUTHOR]

Laboratory measurements of reflectance spectra of rocks and minerals at multiple viewing geometries are important for interpreting spacecraft data of planetary surfaces. However, efficiently acquiring such measurements is challenging, as it requires a custom goniometer that can accommodate multiple, bulky samples beneath a movable light source and detector. Most spectrogoniometric laboratory work to date has focused on mineral mixtures and particulates, yet it is also critical to characterize natural rock surfaces to understand the influence of texture and alteration. We designed the Three‐Axis N‐sample Automated Goniometer for Evaluating Reflectance (TANAGER) specifically to rapidly acquire spectra of natural rock surfaces across the full scattering hemisphere. TANAGER has its light source and the spectrometer's fiber optic mounted on rotating and tilting arcs, with a rotating azimuth stage and six‐position sample tray, all of which are fully motorized and integrated with a Malvern PanAnalytical ASD FieldSpec4 Hi‐Res reflectance spectrometer. Using well‐characterized color calibration targets, we have validated the accuracy and repeatability of TANAGER spectra. We also confirm that the system introduces no discernible noise or artifacts. All design schematics and control software for TANAGER are open‐source and available for use and modification by the larger scientific community. Plain Language Summary: To interpret spacecraft data sets of planetary surfaces, it is important to have similar data sets of rocks and minerals from Earth for comparison. Reflectance spectroscopy is a common technique in planetary science, and spacecraft make spectral reflectance measurements at multiple viewing geometries (with different positions of the sun relative to the planet's surface); therefore, it is important for laboratory spectral reflectance measurements to use multiple viewing geometries as well. However, such measurements are difficult, as they require custom instrumentation (a "goniometer") to control the angles of the light source and spectrometer detector. Most goniometers are designed for measuring small quantities of grains and particulates, but we have designed a new goniometer specifically for measuring the surfaces of bulky, natural rocks: the Three‐Axis N‐sample Automated Goniometer for Evaluating Reflectance (TANAGER). Using motorized, rotating and tilting arcs, TANAGER allows us to rapidly acquire reflectance spectra for multiple samples across a full range of viewing geometries. We have validated the accuracy and repeatability of TANAGER data, and we confirm that the system introduces no discernible noise or artifacts. All design schematics and control software for TANAGER are open‐source and available for use and modification by the larger scientific community. Key Points: Three‐Axis N‐sample Automated Goniometer for Evaluating Reflectance (TANAGER) is an automated goniometer designed to rapidly acquire reflectance spectra of rock surfaces across the full scattering hemisphereWe validated the accuracy and repeatability of TANAGER spectra and confirm that the system introduces no discernible noise or artifactsTANAGER's design schematics and control software are open‐source and available for use and modification by the scientific community [ABSTRACT FROM AUTHOR]

In this article, a numerical calibration procedure for undrained cyclic triaxial tests is presented to evaluate the liquefaction potential in sand and silt samples from mining tailings in northern Chile. The numerical modeling of an axisymmetric specimen involved two stages: isotropic consolidation using the Hardening Soil Small (HSS) model and a cycling phase employing the UBC3D-PLM model to simulate the onset of liquefaction using the criterion that the excess pore pressure ratio R u should exceed 0.8. The results demonstrate that the UBC3D-PLM modeling calibrated with experimental data from cyclic triaxial tests effectively represents the excess pore pressure in both sandy and silty soils from mining tailings. The accuracy of the modeling decreases when a single set of parameters is applied to the same soil at different cyclic stress ratios (CSR), highlighting the need for specific calibrations for each loading. [ABSTRACT FROM AUTHOR]

The gravimetric method is one of the most accurate for determining soil water content (SWC). Several low-cost sensors have been developed to simplify measuring water content in soil by measuring soil moisture. However, the sensor must be calibrated to determine soil moisture parameters accurately. In this research, comparative analysis, and calibration of resistive and capacitive low-cost sensors were carried out. The calibration method for each sensor uses the gravimetric water content (GWC) and volumetric water content (VWC) methods. Measuring changes in SWC using sensors is performed in real time based on internet of things (IoT). Based on the measurements of the capacitive, resistive type 1, and resistive type 2 sensors with three repetitions, the linear regression R2 values were obtained at 0.980, 0.827, and 0.942, respectively. Furthermore, a stability test is carried out to see how stable the sensor is when making measurements over a long period. The result is that the capacitive, resistive type 1, and resistive type 2 sensors have errors 1.971×10- 4, 7.001×10-4, and 6.270×10-4 . Based on the results obtained, capacitive sensors have the highest level of accuracy and stability. Furthermore, capacitive sensors are applied to IoT-based agriculture with long range (LoRa) as communication data. [ABSTRACT FROM AUTHOR]

A key characteristic of a solid-state nuclear track detector (SSNTD) is that as more alpha tracks accumulate on the detector, the likelihood of track overlap will increase, making it difficult to distinguish individual events. This article presents the calibration of an in-house SSNTD using a Makrofol polycarbonate detector and electrochemical etching. The calibration employs a nonlinear log-logistic quantile function, which is nearly linear at low exposures but accounts for the reduced efficiency at high exposures due to overlapping alpha tracks. The function can be fitted to calibration measurements very accurately, eliminating systematic errors previously associated with the method at certain radon exposures. This article explores the uncertainties and detection limits associated with the calibration and outlines methods for their evaluation. Additionally, it includes a preliminary discussion on the method's sensitivity to temperature. [ABSTRACT FROM AUTHOR]

Computer model calibration is a method to identify the unknown parameters of computer models, which is attaining more and more attention now. Most of the existing articles develop the calibration procedure under the assumption that the sample size of the physical experiments is larger than the dimension of the calibration parameters, which would not be satisfied in practice. In this article, we propose a sparse estimator of the calibration parameters and its robust version based on adaptive lasso penalty with adapting to the sample size of the physical experiments and the dimension of the calibration parameters, and the proposed robust estimator can deal with the heavy-tailed error and outliers efficiently. Subsequently, we investigate the nonasymptotic properties of the proposed estimators and obtain an upper bound of l 2 error of the proposed estimators by the concentration inequalities. We conduct some numerical simulations and an application to composite fuselage simulation, which verify that the proposed estimators enjoy nice performance. [ABSTRACT FROM AUTHOR]

The present work deals with examining the effect of measurement errors on the design based statistical properties of the estimators of the population mean when the survey is encountered with some non-response. The technique of sub-sampling of non-respondents have been equipped with calibration techniques to handle the presence of non-response. Two kind of distance measures have been proposed and studied in detail to improve the calibration weights. The data on study as well as auxiliary variable has been assumed to be contaminated with correlated measurement errors. The specific properties of the estimators are derived under simple random sampling without replacement design. The proposed hybrid estimators are supported with numerical illustration incorporating simulation using specific population to show the applicability of proposed techniques practically. It is observed that the absolute relative bias of the estimators increase slightly with increase in error variance. Similarly, the precision of the estimators degrade with increase in error dispersion. Overall the proposed hybrid estimator with the chi-square type distance measure is observed to be remarkably efficient in comparison to the logarithmic type distance measure in presence of non-response and measurement errors. [ABSTRACT FROM AUTHOR]

The shift–rotation method is an absolute testing method that has attracted much attention in recent years, and detection accuracy at the subnanometer level has been achieved. However, for flat absolute calibration, the power aberration cannot be calibrated via the traditional shift–rotation absolute testing method. To solve this problem, we propose a shift–rotation absolute testing method based on autocollimation, which detects the tilt error of the tested mirror caused by the external environment and guides the adjustment of the stage to remove it. Simulations and experiments are conducted to prove this theory. A comparison with liquid experiments shows that the autocollimated shift–rotation method can achieve accurate absolute power aberration and can compensate for the shortcomings of the traditional shift–rotation method. [ABSTRACT FROM AUTHOR]

Aim: The aim of this analysis was to compare a clinical periodontal prognostic system and a developed and externally validated artificial intelligence (AI)‐based model for the prediction of tooth loss in periodontitis patients under supportive periodontal care (SPC) for 10 years. Materials and Methods: Clinical and radiographic parameters were analysed to assign tooth prognosis with a tooth prognostic system (TPS) by two calibrated examiners from different clinical centres (London and Pittsburgh). The prediction model was developed on the London dataset. A logistic regression model (LR) and a neural network model (NN) were developed to analyse the data. These models were externally validated on the Pittsburgh dataset. The primary outcome was 10‐year tooth loss in teeth assigned with 'unfavourable' prognosis. Results: A total of 1626 teeth in 69 patients were included in the London cohort (development cohort), while 2792 teeth in 116 patients were included in the Pittsburgh cohort (external validated dataset). While the TPS in the validation cohort exhibited high specificity (99.96%), moderate positive predictive value (PPV = 50.0%) and very low sensitivity (0.85%), the AI‐based model showed moderate specificity (NN = 52.26%, LR = 67.59%), high sensitivity (NN = 98.29%, LR = 91.45%), and high PPV (NN = 89.1%, LR = 88.6%). Conclusions: AI‐based models showed comparable results with the clinical prediction model, with a better performance in specific prognostic risk categories, confirming AI prediction model as a promising tool for the prediction of tooth loss. [ABSTRACT FROM AUTHOR]

The constant passage of trains on the railways tracks causes, in the course of time, deviations that must be corrected periodically by means of a track calibration process. It consists of designing a new layout, called recreated horizontal alignment (RHA), as close as possible to the deformed center track fulfilling also the technical constraints according to the operational requirements of the railway. In recent years, different models have been proposed to address this task. This paper proposes, first, a new geometrical model that works with continuous variables for the definition of horizontal alignments (HAs) to deal with nonsymmetric transition curves at both sides of a circular curve and second, an optimization algorithm to compute the recreated alignment suitable in sinuous railway sections. This new mathematical model frees the optimization process from the need to previously identify the geometric elements (tangents, circular curves, and transition curves) of the HA. The usefulness of this model is tested with two academic examples showing its good behavior and in a real case study, where this algorithm is compared with the solution adopted by the engineers in a section of the railway line Ourense–Monforte in the NW of Spain. [ABSTRACT FROM AUTHOR]

Due to the frequent occurrence of cracks as structures age, examining the size-dependent responses of cracked micro/nanobeams is critical for safely designing and optimizing high-tech devices such as those used in MEMS/NEMS. With this understanding, we present the size-dependent bending and buckling studies of cracked nanobeams with different boundary edges adopting the well-posed stress-driven nonlocal integral model with discontinuity. Since the presence of a crack, the beam is divided into two segments connected by a rotary spring and a linear spring, whose elastic coefficients can be determined through compliance calibration methods. We propose an efficient method to solve the original integral model directly by converting the integral form of the nonlocal constitutive equation into a general equation through the Laplace transform technique, equipped with three extra constraint conditions. Subsequently, the governing equations of two beam segments are solved by considering the standard boundary and compatibility conditions as well as the extra constraint conditions, while the bending deformation and buckling loads of various bounded nanobeams are presented analytically in the presence of a crack. After verifying the derived formulation, the influence of crack length and location as well as nonlocality on the beam deflections and buckling loads of the first several modes are investigated in detail. Communicated by Rossana Dimitri. [ABSTRACT FROM AUTHOR]

DP-MVSNet, a learning-based multi-view stereo (MVS) network utilizing deformable convolutions, addresses the challenge of weak textures in MVS reconstruction. Surpassing the baseline MVSNet architecture, DP-MVSNet introduces deformable convolution modules for enhanced feature extraction and incorporates deformable 3D convolutions into the view weighting network to optimize cross-view informative feature aggregation. DP-MVSNet achieves advanced results on the DTU (0.338 vs. PatchMatchNet's 0.352) and Tanks & Temples (55.07 vs. PatchMatchNet's 53.15) datasets, generating denser point clouds with finer details and higher scene completeness. Ablation studies confirm the effectiveness of the deformable convolution-based multiscale feature extraction (DMFE) and deformable 3D convolution-based learning-based patch matching (DLP) modules, reducing overall error by 4.0 % compared to the baseline while maintaining competitive runtime. Demonstrating remarkable robustness in reconstructing realistic landscapes without ground truth calibration, DP-MVSNet represents a significant advancement in accurate and efficient 3D reconstruction of complex environments, particularly those with weakly textured regions. [ABSTRACT FROM AUTHOR]

Background: Monolithic or semi‐monolithic detectors are attractive for positron emission tomography (PET) scanners with depth‐of‐interaction (DOI) capability. However, they often require complicated calibrations to determine the interaction positions of gamma photons. Purpose: We introduce a novel hybrid detector design that combines pixelated and semi‐monolithic elements to achieve DOI capability while simplifying the calibrations for positioning. Methods: A prototype detector with eight hybrid lutetium–yttrium oxyorthosilicate (LYSO) layers having dimensions of 25.8 × 12.9 × 15 mm3 was constructed. The energy‐weighted and energy‐squared weighted averages were used for estimating the x‐ (pixelated direction) and y‐positions (non‐pixelated direction). Pseudo‐pixels were defined as discrete areas on the flood image based on the crystal look‐up table (LUT). The intrinsic spatial resolutions in the pixelated and non‐pixelated directions were measured. The ratio of the maximum to the sum of the multipixel photon counter (MPPC) signals was used to estimate the DOI positions. The coincidence timing resolution (CTR) was measured using the average and energy‐weighted average of the earliest n time stamps. Two energy windows of 250–700 and 400–600 keV were applied for the measurements. Results: The pattern of the flood images showed discrete event clusters, demonstrating that simple calibrations for determining the x‐ and y‐positions of events could be achieved. Under 400–600 keV energy window, the average intrinsic spatial resolutions were 1.15 and 1.34 mm for the pixelated and non‐pixelated directions; the average DOI resolution of the second row of pseudo‐pixels was 5.1 mm in full width at half maximum (FWHM); when using the energy‐weighted average of the earliest four‐time stamps, the best CTR of 350 ps was achieved. Applying a broader energy window of 250–700 keV only slightly degrades the DOI resolution while maintaining the intrinsic resolution; the best CTR degrades to 410 ps. Conclusions: The proposed hybrid detector concept was verified, and a prototype detector showed high performance for 3D positioning and timing resolution. The novel detector concept shows promise for preclinical and clinical PET scanners with DOI capability. [ABSTRACT FROM AUTHOR]

The Kennedy model offers a robust framework for modeling forward rates, leveraging Gaussian random fields to accommodate emerging phenomena such as negative rates. In our study, we employ maximum likelihood estimations to determine the parameters of the Kennedy field, utilizing Radon–Nikodym derivatives for enhanced accuracy. We introduce an efficient simulation method for the Kennedy field and develop a Black–Scholes-like analytical pricing formula for diverse financial assets. Additionally, we present a novel parameter estimation algorithm grounded in numerical extreme value optimization, enabling the recalibration of parameters based on observed financial product prices. To validate the efficacy of our approach, we assess its performance using real-world par swap rates in the latter part of this article. [ABSTRACT FROM AUTHOR]

Replacing the endocentric lenses in traditional Phase-Measuring Deflectometry (PMD) with bi-telecentric lenses can reduce the number of parameters to be optimized during the calibration process, which can effectively increase both measurement precision and efficiency. Consequently, the low distortion characteristics of bi-telecentric PMD contribute to improved measurement accuracy. However, the calibration of the extrinsic parameters of bi-telecentric lenses requires the help of a micro-positioning stage. Using a micro-positioning stage for the calibration of external parameters can result in an excessively cumbersome and time-consuming calibration process. Thus, this study proposes a holistic and flexible calibration solution for which only one flat mirror in three poses is needed. In order to obtain accurate measurement results, the calibration residuals are utilized to construct the inverse distortion map through bicubic Hermite interpolation in order to obtain accurate anchor positioning result. The calibrated stereo bi-telecentric PMD can achieve 3.5 μm (Peak-to-Valley value) accuracy within 100 mm (Width) × 100 mm (Height) × 200 mm (Depth) domain for various surfaces. This allows the obtaining of reliable measurement results without restricting the placement of the surface under test. [ABSTRACT FROM AUTHOR]

This paper presents the design and validation of a high-precision angular vibration calibration system based on a laser vibrometer, aimed at meeting the high-precision requirements for measuring small angular vibrations. The system primarily consists of a self-driving angular vibration platform and a laser vibrometer. The platform is isolated from ground interference via an air-floating platform and uses a split-type motor to control the platform, generating specific angular vibrations. Detailed simulations of the platform's modal characteristics and the stability of the spring plates were conducted using the finite element analysis software ANSYS 11. Moreover, fundamental frequency testing and measurement accuracy testing were conducted on the system. Experimental results demonstrate that the system has a fundamental frequency of 2.69 Hz and a maximum measurement error of 0.00172″, confirming the system's effectiveness in dynamic characteristics, stability, and measurement accuracy. This research provides essential technical support for high-precision angular vibration control in spacecraft. [ABSTRACT FROM AUTHOR]

Machine tool accuracy is greatly influenced by geometric and thermal errors that cause positioning deviations within its working volume. Conventionally, these two error sources are treated separately, with distinct procedures employed for their characterization and correction. This research proposes a unified volumetric error compensation approach in terms of a calibration procedure and error compensation model, which considers geometric and thermal errors as a single error source that exhibits temporal variation primarily due to changes in the machine's thermal state. Building upon previous works that introduced a fully automated volumetric calibration procedure capable of characterizing the variation in volumetric error over time, this study extends this methodology, incorporating multiple temperature sensors distributed throughout the machine and generating a digital twin based on a volumetric error compensation model capable of predicting and compensating for the volumetric error over time at any point in the workspace, using temperature measurements and axis positions as inputs. This methodology is applied to a medium-sized milling machine tool. The digital twin is trained and validated on volumetric calibration tests, wherein various controlled heat sources are employed to induce thermal variations while measuring the temperatures in the machine. [ABSTRACT FROM AUTHOR]

As the societal demand for precision in industrial robot operations increases, calibration can enhance the end-effector positioning accuracy of robots. Sampling data optimization plays an important role in improving the calibration effect. In this study, a robot calibration sampling point optimization method based on improved robot observability metrics and a Binary Simulated Annealing Algorithm is proposed. Initially, a robot kinematic model based on the Product of Exponentials (POE) model and a generalized error model is established. By utilizing the least squares method, the ideal pose transformation relationship between the robot's base coordinate system and the laser tracker measurement coordinate system is derived, resulting in an error calibration model based on spatial single points. An improved robot observability metric combined with the Binary Simulated Annealing Algorithm (BSAA) is introduced to optimize the selection of calibration sampling data. Finally, the robot's parameter errors are obtained using a nonlinear least squares method. Experimental results demonstrate that the average end-effector positioning error of the robot after calibration can be reduced from 0.356 mm to 0.254 mm using this method. [ABSTRACT FROM AUTHOR]

The Cohort Study of Mobile Phone Use and Health (COSMOS) has repeatedly collected self-reported and operator-recorded data on mobile phone use. Assessing health effects using self-reported information is prone to measurement error, but operator data were available prospectively for only part of the study population and did not cover past mobile phone use. To optimize the available data and reduce bias, we evaluated different statistical approaches for constructing mobile phone exposure histories within COSMOS. We evaluated and compared the performance of 4 regression calibration (RC) methods (simple, direct, inverse, and generalized additive model for location, shape, and scale), complete-case analysis, and multiple imputation in a simulation study with a binary health outcome. We used self-reported and operator-recorded mobile phone call data collected at baseline (2007-2012) from participants in Denmark, Finland, the Netherlands, Sweden, and the United Kingdom. Parameter estimates obtained using simple, direct, and inverse RC methods were associated with less bias and lower mean squared error than those obtained with complete-case analysis or multiple imputation. We showed that RC methods resulted in more accurate estimation of the relationship between mobile phone use and health outcomes by combining self-reported data with objective operator-recorded data available for a subset of participants. [ABSTRACT FROM AUTHOR]

Narrow endemism is the core of plant diversity in flora of the Mediterranean Basin, which is among the world's 36 biodiversity hotspots. The Mediterranean genus Kundmannia (Careae, Apioideae) comprises just three species, K. sicula , K. anatolica , and K. syriaca. Notably, K. sicula has a wide distribution from the western Mediterranean to Greece, while K. anatolica and K. syriaca occur only in Turkey's Taurus Mountains, which is a phylogeographical hotspot. Initially, the aim herein was to determine the phylogenetic placement of the genus Kundmannia based on DNA sequences, but it later developed to describing a new genus, Antalia gen. nov. and revealing historical events that acted as drivers in the genetic divergence between Kundmannia and Antalia. Both ribosomal and combined chloroplast multilocus sequences were used to unravel the phylogenetic topology. Toward this aim, morphological characteristics were examined. Based on this, Antalia differs from Kundmannia by its shorter bracteoles and petals, the colour of the petals, and smaller fruits. The phylogenetic results revealed that the genus Kundmannia is nested within the tribe Careae and is not monophyletic. Antalia , which is genetically separate from Kundmannia , is described as a new genus. Detailed morphological descriptions and identification keys are provided. Causes of the genetic disjunction between Antalia and Kundmannia were carefully considered in a time-calibrated phylogenetic framework. The Mediterranean Sea has acted as a relatively effective barrier between these genera since the Messinian salinity crisis. The unwinged mericarps seem to have facilitated the work of the barrier. Historical events during and after the Pliocene and allopatric vicariance seem to have been the most important drivers of evolution between Kundmannia and Antalia and within Antalia. [ABSTRACT FROM AUTHOR]

The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte Carlo simulation experiments with a statistical analysis, and we first conduct simulated comparisons of the calibration effects of different CFs while considering the impact of noise. We categorized ten common CFs into four groups based on their functional form and the number of calibration coefficients. Based on functional form, specifically, we defined 1/T = f(lnQ) as a forward calibration function (FCF) and lnQ = g(1/T) as a backward calibration function (BCF). Here, T denotes temperature, and Q denotes the signal intensity ratio. Their performance within and outside the calibration interval is compared across different integration times, smoothing methods, and reference temperature ranges. The results indicate that CFs of the same category exhibit similar calibration effects, while those of different categories exhibit notable differences. Within the calibration interval, the FCF performs better, especially with more coefficients. However, outside the calibration interval, the linear calibration function (which can be considered a two-coefficient FCF) has an obvious advantage. Conclusions based on the simulation results are validated with actual data, and the factors influencing calibration errors are discussed. Utilizing these findings to guide CF selection can enhance the accuracy and stability of PRR lidar detection. [ABSTRACT FROM AUTHOR]

The SWOT satellite, carrying the KaRIN first wide-swath onboard altimeter, was launched in December 2022, and has now delivered more than a year of surface water elevation data over the ocean and inland lakes/rivers. These data are affected by systematic errors which constitute the dominant part of the error budget at scales larger than a few thousands of kilometers. Some strategies for their estimation and calibration were explored during the pre-launch studies with performance estimations. Now, based on the real data, we propose in this study to assess the systematic error budget with statistical methods relying on spectral and co-spectral analysis. From this assessment, suggesting very low error levels (below requirements), we propose the implementation of the calibration algorithms at Level-2 and Level-3 with a few minor adjustments justified by the error spectra. The calibrated products are then validated with usual CalVal metrics. [ABSTRACT FROM AUTHOR]

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments on the recently launched NOAA-21 (previously known as JPSS-2) satellite. The VIIRS, like its predecessors on the SNPP and NOAA-20 satellites, provides daily global coverage in 22 spectral bands from 412 nm to 12 μm. The geometrically and radiometrically calibrated observations are the basis for many operational applications and scientific research studies. A total of 14 of the 22 bands are reflective solar bands (RSBs), covering photon wavelengths from 412 nm to 2.25 μm. The RSBs were radiometrically calibrated prelaunch and have been regularly calibrated on orbit through the onboard solar diffuser (SD) and scheduled lunar observations. The on-orbit SD's reflectance change is determined by the onboard solar diffuser stability monitor (SDSM). We review the calibration algorithms and present the early mission performance of the NASA N21 VIIRS RSBs. Using the calibration data collected at both the yaw maneuver and regular times, we derive the screen transmittance functions. The visible and near-infrared bands' radiometric gains have been stable, nearly independent of time, and so were the radiometric gains of the shortwave-infrared bands after the second mid-mission outgassing. Further, we assess the Earth-view striping observed in the immediate prior collection (Collection 2.0) and apply a previously developed algorithm to mitigate the striping. The N21 VIIRS RSB detector signal-to-noise ratios are all above the design values with large margins. Finally, the uncertainties of the retrieved Earth-view top-of-the-atmosphere spectral reflectance factors at the respective typical spectral radiance levels are estimated to be less than 1.5% for all the RSBs, except band M11 whose reflectance factor uncertainty is 2.2%. [ABSTRACT FROM AUTHOR]

Precast prestressed hollow core (HC) floors are widely used in various applications within the construction sector. Such floors are usually designed as single, simply supported elements, although it is known that individual elements forming the floor interact with each other. This article presents the state of the art regarding load redistribution in HC floors in the light of experimental data, current analytical models and code provisions. While this phenomenon is widely known and recognized, only sparse, and often poorly documented experimental data are available, which represent the basis for the assessment and calibration of analytical models. Moreover, even though the available models and code provisions share similar assumptions, their outcomes are in some cases conflicting. Having recognized the existing knowledge gap, the authors outline future perspectives for the development of consistent analytical and numerical approaches supplemented by new experimental data. [ABSTRACT FROM AUTHOR]

The calibration problem in machine learning classification tasks arises when a model's output score does not align with the ground truth observed probability of the target class. There exist several parametric and non-parametric post-processing methods that can help to calibrate an existing classifier. In this work, we focus on binary classification cases where the dataset is imbalanced, meaning that the negative target class significantly outnumbers the positive one. We propose new parametric calibration methods designed to this specific case and a new calibration measure focusing on the primary objective in imbalanced problems: detecting infrequent positive cases. Experiments on several datasets show that, for imbalanced problems, our approaches outperform state-of-the-art methods in many cases. [ABSTRACT FROM AUTHOR]

Acceleration sensors for structural health monitoring of engineering structures under the influence of environmental factors at low frequencies Structural health monitoring (SHM) is increasingly used to continuously assess the condition of engineering structures. Important assessment parameters are global system properties, such as eigenfrequency, which are measured by accelerometers. micro‐electro‐mechanical systems (MEMS) sensors are often used, but have a high noise level. Alternatively, low‐noise IEPE (integrated electronics piezo electric) sensors can be used, which reliably detect vibrations even with the slightest structural excitation. Another problem is that changes in eigenfrequency due to structural damage are difficult to distinguish from changes in eigenfrequency due to environmental effects. The latter change the properties of both the structure and the measurement system. In order to investigate environmental effects on the measurement system in the field of civil engineering, IEPE accelerometers have been analyzed for their transmission behavior in the low‐frequency acceleration range. It was found that the behavior is not only frequency dependent, but also temperature dependent, while humidity has no influence. These nonstructural effects must be compensated for to ensure robust condition monitoring. IEPE sensors are recommended for civil engineering applications because of their high signal‐to‐noise ratio and ability to reliably detect low‐frequency structural vibrations. [ABSTRACT FROM AUTHOR]