Search

The present paper details the recent advances in three schlieren visualization methods, considering only those applied to obtain values of gas-dynamic parameters of the phenomenon, namely SIV (schlieren image velocimetry), CCS (color-calibrated schlieren) and BOS (background-oriented schlieren). The above-mentioned advances refer to innovative optical configurations - introducing of new optical components or new optical alignments, state-of-the art or improved calibration methods, references to various post-processing algorithms, and present the possibility to improve system efficiency or validate the resulting schlieren data by coupling the schlieren systems with other optical methods.

Traffic Simulating and Evaluating Traffic Patterns at heterogeneous traffic Situations in the Indian Context is extremely increasing. Many researchers from all over the world are trying to explore driving behavior and simulate traffic flow conditions under heterogeneous traffic environments. Simulating Traffic at a Microscopic Level with VISSIM is used in this study identifying every element that contributes to traffic at various traffic stages. Further, the VISSIM model was calibrated based on the desired safety distance and car-following theory. Traffic metrics including flow volume, speed, acceleration, and deceleration were all input parameters in the VISSIM model for simulation purposes. Furthermore, the analysis part of different cases of driving behavior models, such as the default case, and calibration values based on measurement were compared to find out if there was a significant improvement. Moreover, the linear regression model was proposed to understand the calibrated model versus the default case parameters, and it was identified that it is significant when plotting the data. The proposed study results highlighted driving behavior under heterogeneous traffic conditions using VISSIM at Toll Plaza.

Model parameter estimation is a well-known inverse problem, as long as single-value point data are available as observations of system performance measurement. However, classical statistical methods, such as the minimization of an objective function or maximum likelihood, are no longer straightforward, when measurements are imprecise in nature. Typical examples of the latter include censored data and binary information. Here, we explore Approximate Bayesian Computation as a simple method to perform model parameter estimation with such imprecise information. We demonstrate the method for the example of a plain rainfall–runoff model and illustrate the advantages and shortcomings. Last, we outline the value of Shapley values to determine which type of observation contributes to the parameter estimation and which are of minor importance. HIGHLIGHTS Approximate Bayesian Computation is a likelihood-free approach for inverse parameter estimation.; The method is both computationally simple and versatile to use imprecise information.; Censored and binary data are useful observations for parameter estimation.; Shapley values allow us to estimate the significance of a specific type of observation.; The Monte-Carlo based method is computationally expensive.;

In this study, the uncertainty of measurement paths was estimated using selected statistical methods. Specifically, temperature measurements obtained from contact temperature sensors used in a heat transfer test section were investigated. The experiments utilized a drywell temperature calibrator, thermoelements (types K, J, N, and T), and a data acquisition station. Additionally, a certified Pt-100 resistance sensor connected to the temperature meter was considered during measurements. The temperature range for the selected measurement points was 0.3 to 100 °C, covering both increasing and decreasing temperatures. To calculate the expanded uncertainty, both the uncertainty propagation method and the Monte Carlo method were employed. The results were analyzed and found to be similar.

Abstract Background Therapeutic ceiling of care is the maximum level of care deemed appropiate to offer to a patient based on their clinical profile and therefore their potential to derive benefit, within the context of the availability of resources. To our knowledge, there are no models to predict ceiling of care decisions in COVID-19 patients or other acute illnesses. We aimed to develop and validate a clinical prediction model to predict ceiling of care decisions using information readily available at the point of hospital admission. Methods We studied a cohort of adult COVID-19 patients who were hospitalized in 5 centres of Catalonia between 2020 and 2021. All patients had microbiologically proven SARS-CoV-2 infection at the time of hospitalization. Their therapeutic ceiling of care was assessed at hospital admission. Comorbidities collected at hospital admission, age and sex were considered as potential factors for predicting ceiling of care. A logistic regression model was used to predict the ceiling of care. The final model was validated internally and externally using a cohort obtained from the Leeds Teaching Hospitals NHS Trust. The TRIPOD Checklist for Prediction Model Development and Validation from the EQUATOR Network has been followed to report the model. Results A total of 5813 patients were included in the development cohort, of whom 31.5% were assigned a ceiling of care at the point of hospital admission. A model including age, COVID-19 wave, chronic kidney disease, dementia, dyslipidaemia, heart failure, metastasis, peripheral vascular disease, chronic obstructive pulmonary disease, and stroke or transient ischaemic attack had excellent discrimination and calibration. Subgroup analysis by sex, age group, and relevant comorbidities showed excellent figures for calibration and discrimination. External validation on the Leeds Teaching Hospitals cohort also showed good performance. Conclusions Ceiling of care can be predicted with great accuracy from a patient’s clinical information available at the point of hospital admission. Cohorts without information on ceiling of care could use our model to estimate the probability of ceiling of care. In future pandemics, during emergency situations or when dealing with frail patients, where time-sensitive decisions about the use of life-prolonging treatments are required, this model, combined with clinical expertise, could be valuable. However, future work is needed to evaluate the use of this prediction tool outside COVID-19.

Abstract Background Risk prediction models are routinely used to assist in clinical decision making. A small sample size for model development can compromise model performance when the model is applied to new patients. For binary outcomes, the calibration slope (CS) and the mean absolute prediction error (MAPE) are two key measures on which sample size calculations for the development of risk models have been based. CS quantifies the degree of model overfitting while MAPE assesses the accuracy of individual predictions. Methods Recently, two formulae were proposed to calculate the sample size required, given anticipated features of the development data such as the outcome prevalence and c-statistic, to ensure that the expectation of the CS and MAPE (over repeated samples) in models fitted using MLE will meet prespecified target values. In this article, we use a simulation study to evaluate the performance of these formulae. Results We found that both formulae work reasonably well when the anticipated model strength is not too high (c-statistic

Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Attempts to establish regionalization methods for prediction of design flood hydrographs in ungauged catchments have seen little success. Therefore, as well as reviewing key rainfall-runoff model components for design flood estimation with a special focus on event-based conceptual models, this paper covers the aspects of regionalization to promote their applications to ungauged catchments.

Weather radar calibration is a crucial factor to be considered for quantitative applications, such as QPE (Quantitative Precipitation Estimation), which is used as input for weather risks management. The present work proposes a novel approach to the end-to-end radar calibration method through the characterization of the radar weighting functions. These are Gaussian functions that model an additional attenuation factor to the radar received power. This approach, based on the inclusion these parameters, allow the obtainment of a calibrated equivalent reflectivity factor expression for a Doppler dual-polarization weather radar that operates in the X band. To calculate these parameters, a UAS (Unmanned Aircraft System) was implemented for suspending the calibration target with a well-defined cross-section and for measuring its inclination due to wind using an IMU (Inertial Measurement Unit). From its measurements, the position of the target can be estimated, which is essential to the characterization of the weighting functions. Their inclusion within the radar equation, alongside the implementation of the angular measurement system highlight the innovation to the traditional radar calibration methodology that does not contemplate them from the explored state-of-the-art. The reflectivity was compared with the measurements from a disdrometer for a moderate rain event. An average reflectivity difference of 0.75 dBZ and a percent bias of 3.3 % were obtained between the expected and estimated measurements when including these functions compared to the 1.51 dBZ and –62.7 % obtained when disregarding them. These experimental results point out that the proposed method can deliver superior accuracy in the reflectivity estimation.

Introduction The agriculture sector, as the biggest consumer of water to produce more food, has faced the challenge of water shortage. One of the problems ahead in the agricultural industry is the sustainable use of available resources such as land, water, and labor to increase agricultural production and development, which requires proper planning and management policies. Plant models can be used to investigate the long-term effects of quantitative and qualitative changes in irrigation water on crops, soil salinity, evaporation and transpiration, deep infiltration, and surface runoff. One of the widely used plant models is the AquaCrop model, which was presented and developed by the World Food and Agriculture Organization. The Aquacrop model is one of the crop yield estimation models that can be used for a wide range of crops including fodder crops, vegetables, grains, fruits, oil crops, and tubers. In this model, the state of various stresses including water and soil salinity, simulation of lack of irrigation, and crop yield are considered. Various studies have been conducted regarding the calibration and validation of crop forecasting models in our country, and much research has been conducted on wheat at the global level. In this research, the AquaCrop model was used to predict the biomass and grain yield of wheat in Qazvin. This model can be a good substitute for field measurements and can be used in areas where there is a lack of ground information. Materials and Methods In the present research, the data of water wheat cultivation in a lysimeter in Ismailabad, Qazvin were used. The input information of the AquaCrop model includes information on climate, soil, management, and plant characteristics. To calibrate and verify the model, some farm information was needed to be compared with the output of the AquaCrop model. The biomass of the wheat plant was determined by taking random samples of the 0.5×0.5 m2 with two replications per sampling hectare. To measure grain yield in the fields, four samples were taken at the end of the growing season at the end stage. The validated AquaCrop model was used to estimate the effect of three planting dates and three low irrigation conditions on wheat grain yield. In this step, the average regional information around the farms was used so that the implementation of the model is not unique to the conditions of a particular farm. Results and Discussion In terms of the investigated meteorological factors, the model has moderate sensitivity to maximum and minimum temperature and low sensitivity to rainfall. The change in the maximum temperature in this region increases the error of the simulation on average. Regarding the soil parameters, the sensitivity of the model to the crop capacity moisture, wilting point, saturated moisture, and saturated hydraulic conductivity, especially in saturated conditions, is low to medium. The most sensitive of the AquaCrop model was the change in the reference harvest index. The model simulated biomass values with higher accuracy than yield. In the calibration stage, the values CRM, NRMSE, and d for biomass were -0.15, 0.17, and 92% respectively. These values were obtained in the validation stage for biomass -0.1, 0.24, and 92 % respectively, and for yield -0.03, 0.06, and 80 % respectively. By running the model in different climatic scenarios, it was determined that the maximum delay in the planting date is on November 15. A 25% reduction in irrigation water reduced grain yield in wet, normal, and dry years by 15%, 20%, and 28 %, respectively, and a 50 % reduction in irrigation water reduced its amount by 20, 25, and 45 %, respectively. Conclusion Evaluation of the AquaCrop model for common plants in a region plays an important role in comparing crop performance in different conditions. In this research, the ability of AquaCrop 6 model to estimate the yield and biomass of wheat in Ismailabad Qazvin was investigated. The results showed that the model is capable of simulating these factors with high accuracy. The accuracy of the model in biomass simulation was higher than the grain yield. By implementing the calibrated model in different climatic scenarios, planting dates, and irrigation deficits in two regions, it was determined that to achieve optimal performance, the wheat planting date should not exceed 15 November. It was the use of calibration coefficients by spending a long time in the AquaCrop model so that a calibrated model can be used in many areas with proper accuracy. More accuracy in the simulated results can be achieved by using more calibration factors, but it is clear that the use of more calibration factors requires spending more time and money. Finding a general recalibrated model that can be used in large areas is a good solution in crop management at the farm-to-regional scale. Comparing the statistical parameters obtained in this study with previous studies on wheat yield modeling by the AquaCrop model shows that the results of this study are within an acceptable range.

Background: Water quality deterioration is becoming a serious challenge for water utility corporations supplying treated water through the use of a centralized distribution system. After water leaves the treatment plant and enters the distribution system, it is subjected to numerous complex physical, chemical, and biological changes. This study aimed to investigate the major physical factors deteriorating water quality in an aged distribution system. Methods: Deterioration modeling was undertaken using an EPANET computer program. For model calibration processes, data collected from field measurements were used. Descriptive statistics were employed to analyze the data. Water age and residual chlorine were selected parameters to investigate the deterioration level. The identification of major factors posing water quality changes was undertaken by examining distinct physical and operational settings. Results: The maximum water-age variation obtained between two extreme water-use periods was 21.97%. In the same way, the maximum residual chlorine concentration variation obtained was 11.68%. On the contrary, with tested extreme pipe sizes in the study, the maximum water-age variation obtained was only 0.93%. Whereas, the obtained maximum residual chlorine concentration variation between the two extreme pipe sizes was 21.03%. Conclusion: Water use variation poses more water quality degradation than pipe geometry. Water age in aged distribution is rarely influenced by conditions of pipe geometry.

Intermittent water supply systems (IWSSs) are unable to meet customer demands due to water scarcity from the sources or due to economic or/and technical scarcity. Conversion to continuous water supply as a means of tackling IWSSs’ inherent problems of inequitable water distribution, limited water supply hours, high non-revenue water, system operation and maintenance costs, and poor water quality is essential for sustainable water supply. Modelling and optimisation techniques have been used to aid the conversion process, optimisation of the operation of these systems, and guiding leakage reduction actions. However, modelling IWSSs have several challenges. These include the lack of accepted existing modelling techniques that include leakage modelling and the lack of comprehensive methodology for calibrating IWSS hydraulic models under limited calibration data. This study proposes a methodology for calibrating IWSS hydraulic models that include leakage modelling. The proposed methodology involves distinct steps to mitigate the problem of data scarcity, it eliminates the trial-and-error procedure of determining the leakage emitters' coefficients by using optimisation and it presents an approach for estimating the lower and upper bounds of the emitters' coefficients. The methodology was applied to a case study in Zambia. The calibration procedure gave accurate results given the limitation of data. HIGHLIGHTS Development of a comprehensive calibration methodology for intermittent water supply system models that include leakage modelling.; A model calibration approach that selects leakage emitter exponent and coefficients automatically through optimisation.; An approach for setting the lower and upper bounds of the leakage emitter coefficients.;

The inspection tool commonly used during the manufacture and acceptance testing of new machine tools or repair of old machine tools is the test bar. The test bar is a mandrel for calibration aids representing an axis to be checked within a given value, either for run-out or for location (position and orientation) in relation to other machine components. Conditions and performance on the precision of the machine will affect the products produced from the production process. Thus, the use of a test bar for the precision of a machine tool is carried out through a calibration process. At present, the need for test bars for the machine tool calibration process in Indonesia is a product imported from outside (imported), even though the manufacture of test bars allows it to be done independently. In this study, the between centre test bar was redesigned and made based on ISO 230-11 standards with the subject of examination for the straightness of the sledding movement in the horizontal and vertical planes referring to and DIN 8605. The stages of making the between-centre test bar include turning, heat treatment, cylinder grinding, and measurement. The accuracy of the geometry, such as the alignment of the diameter between-centre test bar is carried out using a CMM (Coordinate Measuring Machine) machine. Furthermore, the between-centre test bar is tested on a universal lathe to determine its feasibility through the process of checking the alignment of the spindle axis with the machine table in the vertical and horizontal directions (calibration).

Abstract Background Clinical magnetic resonance imaging (MRI) studies often use Cartesian gradient-echo (GRE) sequences with ~2-ms echo times (TEs) to monitor apparent total sodium concentration (aTSC). We compared Cartesian GRE and ultra-short echo time three-dimensional (3D) radial-readout sequences for measuring skeletal muscle aTSC. Methods We retrospectively evaluated 211 datasets from 112 volunteers aged 62.3 ± 12.1 years (mean ± standard deviation), acquired at 3 T from the lower leg. For 23Na MRI acquisitions, we used a two-dimensional Cartesian GRE sequence and a density-adapted 3D radial readout sequence with cuboid field-of-view (DA-3D-RAD-C). We calibrated the 23Na MR signal using reference tubes either with or without agarose and subsequently performed a relaxation correction. Additionally, we employed a six-echo 1H GRE sequence and a multi-echo spin-echo sequence to calculate proton density fat fraction (PDFF) and water T2. Paired Wilcoxon signed-rank test, Cohen d z for paired samples, and Spearman correlation were used. Results Relaxation correction effectively reduced the differences in muscle aTSC between the two acquisition and calibration methods (DA-3D-RAD-C using NaCl/agarose references: 20.05 versus 19.14 mM; d z = 0.395; Cartesian GRE using NaCl/agarose references: 19.50 versus 18.82 mM; d z = 0.427). Both aTSC of the DA-3D-RAD-C and Cartesian GRE acquisitions showed a small but significant correlation with PDFF as well as with water T2. Conclusions Different 23Na MRI acquisition and calibration approaches affect aTSC values. Applying relaxation correction is advised to minimize the impact of sequence parameters on quantification, and considering additional fat correction is advisable for patients with increased fat fractions. Relevance statement This study highlights relaxation correction’s role in improving sodium MRI accuracy, paving the way for better disease assessment and comparability of measured sodium signal in patients. Key points • Differences in MRI acquisition methods hamper the comparability of sodium MRI measurements. • Measured sodium values depend on used MRI sequences and calibration method. • Relaxation correction during postprocessing mitigates these discrepancies. • Thus, relaxation correction enhances accuracy of sodium MRI, aiding its clinical use. Graphical Abstract

Abstract Background Polygenic risk scores (PRS) quantify an individual’s genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the $$R^2$$ R 2 is a commonly used measure to evaluate prediction accuracy. While the $$R^2$$ R 2 is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. Methods Based on large-scale genotype data from the UK Biobank, we compare three definitions of the $$R^2$$ R 2 on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoprotein A, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. Results Our analysis shows that the choice of the $$R^2$$ R 2 definition can lead to considerably different results on test data, making the comparison of $$R^2$$ R 2 values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the $$R^2$$ R 2 based on the mean squared prediction error (MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis — whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of $$R^2$$ R 2 can provide valuable complementary information. Conclusions Awareness of the different definitions of the $$R^2$$ R 2 on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting $$R^2$$ R 2 values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.

Abstract 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.

Societal Impact Statement Palaeoecological investigations are crucial in understanding millennial to centennial land use and land cover change. By analysing the modern pollen rain from four main vegetation types across Madagascar, this research provides baselines for improving the interpretation of pollen records in palaeoecological studies. This study determines the modern pollen‐vegetation relationship in Madagascar and gives a better understanding of the island's landscapes. Despite some spatial limitations, this approach contributes to resolving the debated topic related to the origin of Madagascar's open ecosystems. Knowing the vegetation history prior to and after human settlement would help guide biodiversity management and its associated ecosystem services. Summary Modern pollen rain contributes to calibrating and defining palaeo‐records relative to the current vegetation of diverse landscapes. Quantifying past vegetation change is important to better frame sustainable and resilient management methods of biodiversity considering human needs. In this paper, we aim to assess modern pollen assemblages in different vegetation types in Madagascar and inform how these could contribute to the interpretation of palaeo‐records. We identified modern pollen assemblages from 21 samples across four major vegetation types in Madagascar, including seven (N = 7) newly generated samples from the tropical dry forest in the Northwest region and 14 datasets downloaded from the African Pollen Database website. The downloaded datasets are from core tops representing modern vegetation previously analysed in the tropical dry forests and spiny thickets, the wooded grassland–bushland mosaic and the littoral humid forest. We evaluated the frequency of different taxa in the samples and conducted multivariate analyses to evaluate the similarities and ecological affinities between samples. Pollen rain reflected the main vegetation types across Madagascar and provided information on the current structure and state of the landscape. Regarding the interpretation of pollen records, pollen rain samples revealed that: (i) Open mosaic ecosystems should contain a percentage of at least 40% grass pollen, informing on the origin of the vegetation in older samples and the state of degradation in more recent vegetation reconstructions; and (ii) Anthropogenic vegetation such as plantations are marked by the abundance of pioneer taxa such as Pinus and/or Eucalyptus spp. with a frequency >20%. Our findings improve the interpretations of pollen records and help differentiate past distribution of forest, open and mosaic vegetation, in Madagascar. It will contribute to further research on human practices, land use and into scenarios of conservation planning not only in Madagascar but also across the tropics.

AbstractIntroduction: Conventional leak detection methods are costly and time-consuming, so attention has recently been drawn to methods that detect leakage by network modeling comprehensively.Methods: In these methods, the difference between the measured pressure and flow rate and those obtained by the model is minimized as a fitness function by calibrating the network and adjusting the nodal demands. It has been established by analysis that the fitness function in itself cannot be a good guide to achieving the minimum point. This drawback was solved by using the ACO method. In addition to the fitness, this method employs a parameter called heuristic guidance to improve its capabilities.Results: This research compared three ACO-based optimization methods. The first was ACO with fixed heuristic guidance already introduced in the literature. In the second method, the heuristic guidance varies by the values of the observed pressures and pressure decline versus the no-leak state. The third is studied and compared by integrating SSEM and ACO models. These methods were investigated on a network derived from the literature and the Birjand network.Conclusion: The results revealed that the methods proposed here achieved more precise results in a shorter time and with fewer iterations.

In the literature on policy design, the topic of citizen or policy-taker compliance, or the lack thereof, has been understood mainly in terms of economistic motivations responding to incentives and deterrents. That is, until recently. A pronounced turn toward behavioral public policy over the last two decades has diversified the set of possible propositions that are available for policy design scholars to study policy-takers. Consequently, how the diversity of policy-taker motivations and behavioral responses can be considered in the design of policy mixes and their implementation is an emerging area of study. Here, we contribute to this research effort by conceptualizing how the motivations of policy-takers affect their compliance response to the calibration of different types of policy instruments. We first elaborate on the inter-relationships between heterogenous policy-taker motivations, policy instrument calibrations, and heterogenous behavioral responses. Next, we use the example of Singapore’s approach to the management of a cyclical environmental disease (dengue) to illustrate the mix of motivations on the part of policy-takers. We then offer some thoughts on how policy instrument calibrations can be deployed to target heterogenous policy-taker motivations and induce greater policy participation.

This article presents a study carried out on a 2 kN·m torque transducer at the Central Office of Measures (GUM) and the Physikalisch-Technische Bundesanstalt (PTB). The weighted least squares method was used to generate the linear regression equations for this torque transducer. The Monte Carlo method and the law of uncertainty propagation were used to calculate the expanded uncertainty. In addition, a creep study was carried out at eight measurement points ranging from 200 N·m to 2000 N·m. The investigations showed that the highest readings of the torque transducer, expressed in electrical units as mV/V, occur within the initial few seconds of the test after the removal of the maximum reference torque.

Objectives: The aim of the study is to assess the influence of the masticatory muscles on the indices of the occlusion, measured with electromyographic (EMG) device Teethan® (Teethan S.p.A., Milan, Italy). The device is designed to assess the effect of occlusion on muscle balance and thus indirectly guides the clinician to the necessary changes in occlusion. This paper aims to determine whether muscles influence the indices characterizing occlusion. Material and methods: Thirty-three persons aged 13-43 (mean 21.33) years, 15 men and 18 women who fulfilled the criteria, were included in the study. All subjects had a good state of dental and periodontal health and complete permanent dentition. EMG recordings of the masseter and temporalis muscles were performed during maximum voluntary teeth clenching on cotton rolls and in centric occlusion. All values were standardized as a percentage of the maximum clenching on cotton rolls. Results: Most of the results of the test on cotton rolls are below normal reference values provided by the manufacturer. Within the normal range are only the Global index and IMPACT. When compared to MVC in centric occlusion, some of the indices were higher than when biting on cotton rolls (POC MM, BAR, Global index), which pointed out the effect of the occlusal contact on these tests. Conclusions: The calibration of the Teethan® device gives reliable and reproducible results. Test during MVC in centric occlusion will provide information on which of the indices are influenced by occlusal factors. Recording a test MVC on cotton rolls gives valuable information for the muscle balance, excluding the influence of the occlusion.

Controlling a remotely operated underwater vehicle (ROV) is an extremely challenging task that requires precise maneuvering and navigation in complex and often unpredictable environments. The operator faces numerous difficulties, including limited visibility and communication constraints, and the need to interpret data from various sensors. This paper describes a method for calibration of a wearable system equipped with inertial measurement unit (IMU) sensors that control the underwater manipulators. To implement a solution that allows the robot to be controlled by the operator's hand movements, it is necessary to measure the movement of the arm. This task is carried out using the IMU sensors, which are mounted in appropriate places on the ROV operator's suit to allow mapping the movement of his/her upper limbs. These movements are transferred to the manipulator's arms on the ROV, making it possible to interact with the environment by - manipulating objects under-water.

Sesame, a crucial oilseed crop in Ethiopia, ranks second only to coffee in its importance as an exported agricultural commodity. However, inadequate soil fertility management has hampered its productivity despite its substantial international market demand. Hence, this study was conducted to model the response of sesame to different nitrogen fertilizer levels using the AquaCrop model, and to assess the capability of the model as a decision-support tool for optimizing soil fertility management strategies in the study area. The experiment was laid out in a randomized complete block design, consisting of four nitrogen fertilizer rates (0, 23, 46, and 69 kg/ha nitrogen) and three distinct sesame varieties (Setit-1, Setit-2, and Humera-1). Over the course of the three cropping seasons, data on soil physical and chemical properties, crop growth, yield and yield components were collected for each treatment. Evaluation of model performance relied upon established metrics of coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (N-RMSE), model efficiency (E), and degree of agreement (D). Analysis of results revealed the AquaCrop model appropriately calibrated for simulation of soil water content, showing R2 values ranging from 0.92 to 0.98, RMSE values varying from 6.5 to 13.9 mm, E values from 0.78 to 0.94, and D values from 0.95 to 0.99. Similarly, simulation outputs for aboveground biomass (AB) demonstrated good accuracy of the model, with R2 values varying from 0.92 to 0.98, RMSE values ranging from 0.33 to 0.54 tons/ha, and D values from 0.9 to 0.98. Notable accuracy was also observed in the simulation of canopy cover (CC), revealing R2 values between 0.95 and 0.99, and RMSE values ranging from 5.3 to 8.6 %. In conclusion, this study substantiates the successful calibration and validation of the AquaCrop model for predicting sesame response to diverse nitrogen fertilizer levels. The performance of the model in predicting soil water content, CC, AB, and yield highlights its potential as a valuable tool for optimizing soil fertility management and enhancing sesame cultivation practices in Ethiopia.

In a low-cost system with limited hardware capacity, due to the fast speed of the conveyor and the delay in shooting and processing time, a part of the workpieces may be out of view of the camera. As a result, the information received is incomplete. To solve this challenge, this paper proposed a method to accurately estimate the position of workpieces with incomplete information. First, a complete image of the workpiece in the horizontal position is captured and stored. In the case that a part of the workpiece lies outside the image, the contour matching based on the Hu moments between the contour of the incomplete part and different parts of the complete workpiece is conducted to determine which part of the workpiece has been taken by the camera (called the matching part). Then, the distance between the centroid of the matching part and the entire workpiece is calculated. The difference between the orientation angle of the current incomplete part and the matching part is the orientation angle of the entire current workpiece (with a part out of the image boundary). From the centroid distance and orientation angle, the actual centroid of the entire current workpiece can be calculated. Finally, the calibration method is used to transform the pose of the workpiece into the robot base frame. The experimental results demonstrate that the accuracy of the centroid estimation is 2.2 pixels and 1.8 mm in the image and the 3D space, respectively.

Abstract Tropical forest diversity governs forest structures, compositions, and influences the ecosystem response to environmental changes. Better representation of forest diversity in ecosystem demography (ED) models within Earth system models is thus necessary to accurately capture and predict how tropical forests affect Earth system dynamics subject to climate changes. However, achieving forest coexistence in ED models is challenging due to their computational expense and limited understanding of the mechanisms governing forest functional diversity. This study applies the advanced Multi‐Objective Population‐based Parallel Local Surrogate‐assisted search (MOPLS) optimization algorithm to simultaneously calibrate ecosystem fluxes and coexistence of two physiologically distinct tropical forest species in a size‐ and age‐structured ED model with realistic representation of wood harvest. MOPLS exhibits satisfactory model performance, capturing hydrological and biogeochemical dynamics observed in Barro Colorado Island, Panama, and robustly achieving coexistence for the two representative forest species. This demonstrates its effectiveness in calibrating tropical forest coexistence. The optimal solution is applied to investigate the recovery trajectories of forest biomass after various intensities of clear‐cut deforestation. We find that a 20% selective logging can take approximately 40 years for aboveground biomass to return to the initial level. This is due to the slow recovery rate of late successional trees, which only increases by 4% over the 40‐year period. This study lays the foundation to calibrate coexistence in ED models. MOPLS can be an effective tool to help better represent tropical forest diversity in Earth system models and inform forest management practices.

Abstract The Terminal Tracking Camera (TTCam) imaging system on the NASA Lucy Discovery mission consists of a pair of cameras that are being used mainly as a navigation and target acquisition system for the mission's asteroid encounters. However, a secondary science‐focused function of the TTCam system is to provide wide‐angle broadband images over a large range of phase angles around close approach during each asteroid flyby. The scientific data acquired by TTCam can be used for shape modeling and topographic and geologic analyses. This paper describes the pre‐flight and initial in‐flight calibration and characterization of the TTCams, including the development of a radiometric calibration pipeline to convert raw TTCam images into radiance and radiance factor (I/F) images, along with their uncertainties. Details are also provided here on the specific calibration algorithms, the origin and archived location of the required ancillary calibration files, and the archived sources of the raw calibration and flight data used in this analysis.

Abstract Target localization for distributed radars requires precise knowledge of the calibration errors in time, frequency, and space. Previous studies focus on the sensor location error and sensor directional error in space. This paper concentrates on the system errors in time and frequency which directly affect the targets estimation of range and velocity. Based on linear frequency modulation waveform, the impact of the system errors including sample interval, pulse repetition interval, and centre frequency are analysed. An alternating optimization algorithm is proposed to achieve the target localization, velocity estimation and system errors estimation simultaneously. Simulation shows the effectiveness of the algorithm and the influence on localization due to varying number of targets and radars.

Abstract To address the increasing demand for diurnal information on trace gases and aerosols, a series of geostationary (GEO) satellite programs called GEO‐constellation have been initiated, with the launch of the Geostationary Environment Monitoring Spectrometer (GEMS) onboard Geostationary Korea Multi‐Purpose Satellite 2B (GK2B). To assess the sensor performance of GEMS in orbit, the current work suggests employing an inter‐calibration methodology involving the Advanced Meteorological Imager (AMI) aboard its twin satellite, GK2A. Twin satellites have a significant advantage in obtaining collocation data sets across diverse spatiotemporal, angular, and atmospheric conditions, enabling rigorous collocation criteria effectively reducing mismatch uncertainty. The results present robust correlation coefficients over 0.99, revealing the current calibration characteristics of the sensors. This research emphasizes the advantages of the GEO‐GEO inter‐calibration, particularly the capability of analyzing spatial and temporal dependencies. These findings confirm the mutual benefit of utilizing the sensors in similar configurations, highlighting their importance for future satellite monitoring endeavors.

This paper illuminates the utilization of SUMO as a powerful tool for addressing real-world traffic management issues. There is a gap in testing and validating solutions to in-field conditions due to the high cost and complexity of urban and suburban road networks. The validation step is often skipped, which can lead to a higher risk in implementing sophisticated solutions that exist in our multimodal transportation environment. This challenge is addressed by introducing simulations as a crucial preliminary step before real-world application. Accurate simulations require detailed data on intersection geometries, vehicle distribution, and driver behavior to accurately mirror real-world conditions. To meet these criteria, detailed sensor data on trajectories, types of road users, and their locations are extensively employed. This data forms the foundation for calibrated traffic simulations by NoTraffic™ . In conclusion, an in-depth demonstration of the method used to address a real-world traffic problem with SUMO is provided, emphasizing SUMO’s effectiveness in building confidence for deploying solutions in the field.

Accurate traffic models are of decisive importance for well-founded traffic engineering and represent the basic framework for comprehensive simulation studies as modelling of traffic demand. Using traffic count and speed measurements of road segments is a common approach for the calibration of a realistic traffic simulation although the data acquisition process can be at very extensive costs. From an academical point of view, there have been many studies addressing the problem of calibration. In this respect, the microscopic simulation software SUMO offers the usage of the tools flowrouter and routesampler for generating network simulations on the base of traffic count measurements. In this paper, we propose a robust method for the calibration of microscopic traffic simulations by using vehicle count and speed measurements from collected GPS-data. The developed approach is a two-step optimization process: The application of integer linear programming (ILP) as a priori optimization is followed by adopting an evolutionary algorithm for minimizing the a posteriori deviation between real and simulated traffic data. As a proof of concept, the proposed method is tested in a subnet-work model of the inner city of Friedrichshafen and compared with the ready-to-use tools from SUMO. The suggested method indicates a promising correlation between simulated and real traffic data showing better calibration results in comparison to the aforementioned functions SUMO provides. Since the approach is network-independent, it also offers the possibility of large-scale traffic calibration.

A fully coupled computational fluid dynamics (CFD) and discrete element method (DEM) model was calibrated using a draw down test (DDT) under submerged conditions. Momentum smoothing and cell clustering were used to model particles that were larger than the cells. The DEM input parameter values were initially set equal to those calibrated for the dry conditions. Under submerged conditions, results showed that the particle-particle coefficient of friction and the drag modifier had an influence on the results. It was found that the drag modifier had to be calibrated, while the particle-particle coefficient of friction, calibrated under dry conditions, could be used for the submerged conditions. A vertical suction pipe validation experiment was conducted. The suction pipe had a constant diameter, but the fluid velocity and the distance the pipe opening was held from the granular bed were varied. The amount of mass (particles) removed as well as the size of the cavity that formed in the material bed were measured and compared to model predictions. The results showed that using the parameter values calibrated in the DDT, too much material was removed (error of 30%). Removing the drag modifier (setting it equal to unity) significantly improved the results (error of 6%). It is concluded that due to the difference in flow mechanism (particle-induced in the DDT versus fluid-induced in the suction pipe), the DDT is not a suitable experiment to calibrate the input parameter values for a suction pipe. It is proposed that the flow mechanism and dynamics of the granular material and the fluid in the calibration experiment should be similar to that of the final application being investigated.

The paper deals with the topic of resistance of bonded headless post-installed anchors to uncracked concrete, in particular with the calibration of selected models predicting their ultimate tensile load. In particular, the optimization of two models for predicting the tensile capacity of an anchor is investigated in this paper. First model is based on the determination of the ultimate capacity based on separated failure modes (failure of the extraction of concrete cone and the bond failure). The second optimised model, proposed in this paper, summarizes the effect of both of these parameters into a single exponential function. A model combining the effect of concrete strength and adhesive strength has the potential to better capture the true nature of failure in which both materials are involved. In order to compare these models, an extensive database of experimental results was compiled (including own experiments and also results from various authors). The calibration consisted in finding the most appropriate values of the individual input parameters of the models to fit the experimental results as closely as possible. The models for predicting the tensile capacity of anchors are multiparametric. Therefore, a method using elements of genetic algorithms was used for optimization, suitable for this purpose. Several possible statistical evaluation criteria were used for the evaluation of the fit of the models. The optimization of the models showed that the proposed model combining the effect of concrete strength and bond strength can be optimized to better fit the experimental results.

Introduction and GoalDue to the large volume of human and commercial activities, one of the most important goals of infrastructure is to collect and transfer urban runoff, control floods and prevent flooding in cities. To correctly estimate the runoff and characteristics of hydrological units and channels, it is important to use appropriate hydrological and water models. Considering the importance of this topic, this research was conducted with the aim of estimating the urban runoff of the Malayer to determine the potential flooding points using a storm water management model (SWMM).Material and MethodsOn the basis of available technical reports, the area of the sub-watersheds, nodes and main channels of the city were first determined. Flow direction and slope of the area was determined using a digital elevation model and 11 sub-watersheds were identified. In addition, 11 nodes were determined, regardless of the initial water depth and water level. Using the daily rainfall data of the Malayer synoptic station during the 1992–2020 statistical period, a nine-hour rainfall with a return period of 2 years was calculated and entered into the model. The geometrical shape of the channels was an open rectangle and the flow trend in the channels was determined using the kinetic wave method. The size of the maximum flow of water through the channels was calculated using the relationship between the cross-section of the flow and the speed of the flow a 9-hour the rainfall with a return period of 2 years. To calibrate the model, the variables of percentage of impervious areas, pond storage and roughness coefficient of impervious areas were used in the change range of the allowed modification range. In order to evaluate the model, Nash-Sutcliffe efficiency coefficient and the root mean square error were used.Results and DiscussionThe results showed that after optimizing the sizes of the variables, the Nash-Sutcliffe efficiency coefficient and the root mean square error were 0.73 and 0.02, respectively. Of the total rainfall of 14.54 mm, 5.53 mm was related to infiltration losses, 7.55 mm was related to surface runoff, and 1.45 mm was related to pond storage. The results showed that the sub-watersheds that were in the north of the city and overlooking the heights and leading to node number 10 had more volume and drainage and it is necessary to revise the design and expansion of the channels in this area. In addition, the sub-watersheds of the western part of the city (sub-watersheds no. 4) and the southwestern part (sub-watersheds no. 8) had the highest and lowest flood potential of 0.651 and 0.547, respectively.Conclusion and SuggestionIn this research, the results showed that almost half of the city would be affected by flood risks in the event of rains. Therefore, the current drainage network does not have the necessary efficiency to discharge urban runoff in the northern part of the city, and it is necessary to determine the optimal dimensions of the channels. Because the eastern elevations of the studied area are snow-covered and considering the geology of the area, in future research, models capable of calculating the runoff caused by snow melting should be used.

Microscopic traffic simulation models offer an effective way to analyze and assess different transportation systems thanks to their efficiency and reliability. As traffic management issues become more prevalent, notably in urban areas, simulation tools enable a significant opportunity to replicate real-world conditions before implementation. Therefore, the calibration of traffic simulation models plays a substantial role in obtaining accurate and confidential results. Nowadays, urban regions are facing the challenge of restricted space for developing traffic solutions. As a consequence of environmental restrictions, the use of mini-roundabouts rather than larger roundabouts is increasing. Based on the given literature review, it is seen that not much attention was given to the complex modeling and calibration of microsimulation models of mini-roundabouts and unsignalized intersections. The objective of this study is to offer the calibration of microscopic traffic simulation of urban road network, including closely located mini-roundabouts and unsignalized intersection. To this end, an open-source tool called SUMO (Simulation of Urban Mobility) was utilized as a simulation environment in this study. The necessary data for developing a microsimulation model in SUMO was gathered using a videography technique. The traffic count data and speed were considered performance measures between field observations and simulation outputs. The routeSampler tool of SUMO, which has recently emerged in the literature, was used to match traffic count data and the corresponding time interval for traffic volume data calibration. The calibration of car-following model parameters using a trial-and-error approach was employed based on mean absolute percent error (MAPE) between simulated speeds and field-measured speeds. According to the findings of the study, the simulation model fulfilled the calibration aims of the FHWA guideline and is suitable for further research.

Abstrak Data hidrologi dalam menentukan besaran debit adalah curah hujan dan klimatologi. Salah satu tempat di sungai Cimanuk yang memiliki pos duga air adalah Cimanuk-Bojongloa. Data debit selain dari hasil observasi, juga bisa menggunakan pemodelan. Model yang digunakan pada penelitian ini adalah model NRECA dan model Sacramento. Data curah hujan untuk kalibrasi dengan empat pos, yaitu Pamegatan, Pangauban, Kapakan, dan Bayongbong dari tahun 2017 sampai 2021. Curah hujan rata-rata dihitung dengan metode Thiessen. Kalibrasi model untuk memperoleh parameter kalibrasi terbaik sehingga debit pemodelan akan mendekati debit observasi. Pemodelan debit menggunakan data harian dari tahun 2012 sampai 2021. Hasil kalibrasi pada model NRECA, yaitu PSUB, GWF, dan Wo adalah masing-masing 0,9; 0,2; 600; dan nilai NSE adalah -1,557. Pada model Sacramento hasil kalibrasi adalah UZTW sebesar 25; UZFW sebesar 400; LZTW sebesar 600; LZFS sebesar 20; dan LZFP sebesar 40. Nilai NSE pada model Sacramento adalah 0,471. Kata kunci: kalibrasi, NRECA, NSE, Sacramento Abstract Hydrological data in determining the amount of discharge is rainfall and climatology. One of the places on the Cimanuk river that has a water observation post is Cimanuk-Bojongloa. Discharge data apart from measurement results, can also use modeling. The models used in this research are the NRECA model and the Sacramento model. Rainfall data for calibration with four station namely Pamegatan, Pangauban, Kapakan, and Bayongbong from 2017 to 2021. Average rainfall is calculated using the Thiessen method. Calibrate the model to obtain the best calibration parameters so that the modeled discharge will be close to the observed discharge. Discharge modeling uses daily data from 2012 to 2021. The calibration results for the NRECA model, namely PSUB, GWF, and Wo are 0.9 each; 0.2; 600; and the NSE value is -1.557. In the Sacramento model the calibration results are UZTW 25; UZFW 400; LZTW 600; LZFS 20; and LZFP 40. The NSE value in the Sacramento model is 0.471. Keywords: calibration, NRECA, NSE, Sacramento

Measurements of the fair-weather electric field in mountainous areas are affected by the terrain, and therefore need additional calibration to be included in the global field picture. To do this, it is proposed to solve the three-dimensional electric current continuity problem of the atmosphere in the region between the Earth's surface and the ionosphere. As an example, the neighborhood of Klyuchevskaya Sopka is considered. With an increase in the height of the plateaus, the fair-weather electric current density above them increases, and the electric field strength decreases. A one-dimensional model of atmosphere conductivity is not applicable for terrain with steep slopes. A comparison of the daily-seasonal diagrams constructed according to the data of the Carnegie Cruise VII and according to the Tomsk Observatory showed the similarity of variations of the fair-weather electric field strength in such different places on the Earth. The field over the sea is about half as small as over low-lying land at the same time.

Across the world best practice, the flood hazard and risk maps are core instruments used for the development and implementation of flood management policies. These maps sit as base information for developing flood management strategies and plans, designing of new flood defence schemes and assessment of the existing flood scheme performance, elaboration of emergency and action plans, territorial planning (zoning and permissions), land use control, climate changes impacts evaluation, insurance industry etc. Romania did remarkable steps in developing high quality flood hazard and risk maps as core action for an integrated flood risk management. In the second cycle of Floods Directive 2007/60/EC implementation, Romania developed and reported to European Commission (EC) the hazard and risk maps for 526 APSFRs (Areas with Potential Significant Flood Risk). The hazard maps built in second cycle are the result of detailed models developed based on high resolution Lidar (0.5m), calibrated hydrological data and advanced modelling technics. Crasna River is one of the watercourses from the Somes-Tisa Basin which benefited from advanced 2D hydraulic modelling having in-place all the existing infrastructure and which has been well - calibrated on the flood event recorded in 2015. The calibration of the model on 2015 flood event was performed both for flows and levels. The article aims to explicitly present the performance of the existing defence system of Crasna river during extreme flood events with different magnitudes having in hand these high-quality hydraulic model and hazard maps.

Methods of evaluation of interlaboratory comparison results are prescribed by ISO standards. Ruler is probably one of the most widely used length measuring instrument in the world. For laboratories that plan to be accredited for the calibration of Ruler, the acquisition of a specialized Measuring bench is not economically feasible. Confidence in the measured result depends on the measurement uncertainty, and where deficiencies are observed, the values that contribute to the measurement uncertainty should be investigated. This paper describes an interlaboratory comparison for a small number of laboratories on the example of a Measuring bench for calibrating Ruler and indicates the procedure for problem identification; diagnosis of causes; consideration of solutions; initiation of action for improvement and monitoring of the results of applied solutions. The authors documented the problem and listed the elements that contributed to "Unsatisfactory" result in interlaboratory comparisons. They described the actions they took to overcome the "Unsatisfactory" result, analyzed whether the proposed solutions led to "Satisfactory" result and took actions accordingly. The paper presents the results of the international interlaboratory comparison, which are a confirmation of the competence of the personnel, measuring calibration equipment and calibration method.

The accuracy of microwave remote sensing relies on the calibration of the radar measurement. It is important to estimate the radar cross-section (RCS) using a passive corner reflector (CR) or active transponder to evaluate the quality of imaging radar data. A strong and consistent RCS can be achieved by the acquisition of radar signals concentrated at specific angles during the CR calibration procedure. There are several types of CR depending on the shape and size such as triangular trihedrals, square trihedrals, dihedrals, spheres, or cylinders. In this study, we examine the RCSs using three types of CR with Ku-band ground-based real aperture radar equipment, the Gamma Portable Radar Interferometer-II. It can be easily deployed to acquire fully polarimetric radar observations. The initial experiment was conducted at Busan Sam-nak Auto Camping Site on November 1, 2023. The amplitude images show much higher backscattered radar signals at the CR location, whereas relatively lower power has been captured in the surrounding areas. The attenuation factors in the radar receivers could be useful to prevent saturation around the CR location at the line-of-sight direction. The experiment indicates that the different levels of the RCS measurements from three types of CRs could be utilized for calibration study with fully polarimetric radar observations.

This comprehensive study delves deeply into the intricate domain of optimizing Micro-electromechanical Systems (MEMS)-based navigation sensors for aerospace vehicles. It entails a meticulous examination of MEMS sensors, focusing on their role in guidance, navigation, and control, with particular emphasis on MEMS inertial sensors and crucial performance metrics. The study investigates a spectrum of techniques for sensor optimization, including strategies for enhancing fabrication and production through smart structures and mathematical modeling. Additionally, it explores methodologies and mechanisms for improving navigation sensor fabrication, along with the incorporation of optimizer techniques to manage computational complexities effectively. The key findings underscore the challenges tied to material selection and structural intricacies in optimizing these sensors for aerospace applications. Integration of sensors into integrated circuits, development of advanced mathematical models, and harmonization with artificial intelligence algorithms are vital for boosting sensor performance, while calibration and error mitigation during user deployment are essential. Furthermore, the study underscores the imperative for addressing limitations in sensor accuracy and precision through refined calibration mechanisms and error correction processes. The trajectory for future research involves advancing material selection, mathematical models, and innovative calibration techniques to comprehensively enhance sensor performance and reliability in aerospace applications.

A calibration system was designed to evaluate the accuracy of linear optical encoders at the micron level in a fast and economical manner. The system uses a commercial interferometer and motor stage as the calibrator and moving platform. Error analysis is necessary to prove the effectiveness and identify areas for optimization. A fixture was designed for the scale and interferometer target to meet the Abbe principle. A five-degree-of-freedom manual stage was utilized to adjust the reading head in optimal or suboptimal working conditions, such as working distance, offset, and angular misalignment. The results indicate that the calibration system has an accuracy of ±2.2 μm. The geometric errors of the calibration system, including mounting errors and non-ideal motions, are analyzed in detail. The system could be an inexpensive solution for encoder manufacturers and customers to calibrate a linear optical encoder or test its performance.

Abstract Background In southern central rift valley of Ethiopia, maize is an important crop because of its adaptation to wider agro-ecologies and higher yield potential. However, most cultivars were not parameterized to include in the database of Decision Support System for Agro-technology Transfer (DSSAT). As a result simulation of growth and yield of those cultivars was not possible under changing climate. Methods Two set of independent crop, management and soil data were used for calibration and validation of genetic coefficients of maize cultivars (BH-540, BH-546, BH-547, Shala and Shone) under condition of historic weather (1990–2020). Later, we simulated the growth and yield of maize using twenty multimodel climate ensembles across RCP 4.5 and 8.5 during early, medium and late century across Shamana, Bilate, Hawassa and Dilla clusters using DSSATv4.8 model. Results Cultivars BH-540, BH-546, BH-547, Shala and Shone produced yields of 5.7, 5.4, 5.2, 6.9 and 7.4 t ha−1 with the corresponding error percentage of − 0.1, − 0.8, − 1.0, − 6.1 and 2.6%. The results of normalized root mean square were 1.14–4.2 and 3.0–3.9%, for grain yield during calibration and validation, respectively showing an excellent rating. The simulation experiment produced 5.4–9.2 t ha−1 for grain yield of maize cultivars across the study areas, which is likely to fall close to 63.3% by 2070 if right adaptation options are not introduced necessitating switch in cultivars and production areas. Conclusions There is critical need for reduction of GHGs emissions, generation of innovative adaptation strategies, and development of drought and heat stress tolerant maize cultivars. Hence, researchers and policy makers shall act with utmost urgency to embark with breeding programs that target climate change adaptation traits in maize crop.

The traditional method of joint calibration of fixed baseline interferometer with multiple errors requires more than three calibration emitters to achieve real-time calibration of the interferometer in orbit, but the performance of the traditional method will be reduced or even failed when the number of calibration emitters is insufficient. For the situation that the number of calibration emitters is insufficient, a single or double emitters calibration method for fixed baseline interferometer is proposed. Under the condition of relative motion between the interferometer and the calibration emitters, a set of phase differences of calibration signals is obtained firstly by multiple measurements, and a set of equations about the systematic errors of the interferometer is listed by using the obtained phase differences, finally the systematic errors are solved by the Penrose inverse matrix assisted iterative least squares method. The simulation results verify the feasibility of the proposed method. On this basis, the influence of the emitter selection on the positioning performance of the interferometer after calibration is analyzed qualitatively by means of numerical simulation, decomposing the parameters of the emitter selection. The analysis results show that the calibration emitter located near the sub-star in the single emitter case or two calibration emitters located far apart both can make the interferometer obtain better positioning performance after calibration.

Recent investigations have noted that using a hybrid arrangement of Soil and Water Assessment Tool (SWAT) and multi-layer perceptron (MLP) has high efficiency in runoff prediction. In this research, in addition to using the SWAT and MLP models, an optimized algorithm called Mutated SunFlower Optimization (MSFO) algorithm has been proposed to predict better runoff, which improves the results of prediction runoff by decreasing the error percentage in the MLP model. For this purpose, first, runoff modeling is used to assess the efficiency of the SWAT system. The model's verification and calibration have been performed using data from the previous 30 years of statistics. Then, the flow stream simulated by the SWAT method is evaluated with the observational data and applied as the inputs to the MLP model, and finally, runoff is predicted through the MLP model, and MSFO is used in the MLP model to obtain better results for runoff prediction. The results show that the values of statistical indices R2, RMSE, NSE, and RE give satisfying agreement for runoff forecast in the SWAT–MLP/MSFO model with values of 0.83, 1.68, 0.51, and −0.1. HIGHLIGHTS Two versions of the SWAT model are utilized for forecasting runoff.; The SWAT–MLP model is based on Multi-Layer Perceptron networks.; The SWAT–MLP is optimized based on an improved metaheuristic.; Modified SunFlower Optimization algorithm is used for optimizing SWAT–MLP.; The SWAT–MLP\MSFO model simulates the runoff more accurately.;

Abstract To date, publications have shown that compositions of oral microbiota differ depending on their habitats (e.g. tongue, tonsils, pharynx). The absence of set standards for the choice of the areas and conditions of material collection makes the oral microbiome one of the most difficult environments for a comparative analysis with other researchers, which is a meaningful limitation during an assessment of the potential effects of microorganisms as biomarkers in the courses of various human diseases. Therefore, standardisation of basic conditions of a dental examination and collection of material for the next generation sequencing (NGS) is worth attempting. The standardisation of the dental exam and collection of the clinical materials: saliva, swab from the tongue ridge, hard palate, palatine tonsils and oropharynx, supragingival plaque and subgingival plaque. Protocol involved the patients (n = 60), assigned to 3 groups: I—COVID-19 convalescents who received antibiotics, n = 17, II—COVID-19 convalescents, n = 23 and III—healthy individuals, n = 20. The collected biological samples were used to conduct NGS (16S rRNA). The conditions of patient preparation for collecting biological materials as well as the schedule of dental examination, were proposed. Based on the research conducted, we have indicated the dental indicators that best differentiate the group of COVID-19 patients (groups I and II) from healthy people (group III). These include the DMFT, D and BOP indices. The use of alpha and beta diversity analysis provided an overall insight into the diversity of microbial communities between specific niches and patient groups. The most different diversity between the studied group of patients (group II) and healthy people (group III) was noted in relation to the supragingival plaque. The order of activities during the dental exam as well as while collecting and securing clinical materials is particularly important to avoid technical errors and material contamination which may result in erroneous conclusions from the analyses of the results of sensitive tests such as the NGS. It has been shown that the dental indices: DMFT, D number, PI and BOP are the best prognostic parameters to assess the oral health. Based on beta diversity the most sensitive niche and susceptible to changes in the composition of the microbiota is the supragingival plaque. The procedures developed by our team can be applied as ready-to-use forms in studies conducted by other researchers.

Abstract Background Since color measurements are relative, the discrepancy among different instruments is alarmingly high. This multicenter study evaluated the effectiveness of instrument calibration and inter-instrument harmonization of different spectrophotometers with the same optical geometry using tooth-colored, translucent dental materials. Methods The coordinating center (CC) spectrophotometer was calibrated using the NPL Ceram Series II set. Two sets of 10 specimens, labeled 1 to 10 and I to X (10 mm in diameter and 1 mm thick), were tested at CC and three research sites (RS1, RS2, and RS3) using the same d/8° optical geometry spectrophotometers. Calibration factors were calculated for each material and site to obtain the average calibration factors for sets 1–10, set I-X, and the combination of both. The differences among the non-corrected and corrected reflection values were calculated using CIEDE2000 (DeltaE00) and CIELAB (DeltaEab) color difference formulas and were submitted to ANOVA and Tukey test (α = 0.05). Results A significant decrease of color differences between non-corrected as compared to corrected measurements was recorded for all CC-RS and RS-RS comparisons. The reduction of DeltaE00 values between non-corrected and corrected for CC-RS1, CC-RS2, and CC-RS3 were 83.1%, 77.2%, and 73.6%, respectively. The corresponding DeltaE00 values for RS1-RS2, RS1-RS3, and RS2-RS3 comparisons, indirectly compared in the experiment, were 84.2%, 82.8%, and 68.5%, respectively. There was a significant reduction of DeltaE00 and DeltaEab color difference for all combined RS pairs and each of three RS pairs, corrected with one of two specimen sets calibration factors separately. Conclusions Calibration and harmonization of color measurements in dentistry using tooth-colored, translucent restorative materials significantly decreased measurement discrepancies between the coordinating center and research sites and among pairs of research sites.

To prevent severe safety accidents caused by primary coolant leakage in pressurized water reactors (PWRs), it is necessary to monitor the leakage rate of key parts of the reactor coolant pressure boundary (RCPB) in order to take timely countermeasures. The primary coolant of PWRs is usually high-pressure supercritical water containing boron, which can produce 18F in the high neutron flux environment of the reactor. The leak rate of the RCPB can be inferred by measuring the activity concentration of the radioactive 18F aerosol produced by the coolant leaking into the containment. A measurable sample source is usually obtained by extracting sample gas containing 18F aerosol from the containment and filtering it in the monitor. The measurement method used by the monitor is to screen the γ signal by using the β signal as the gate signal, which is actually a γ relative measurement method using the coincidence method to reduce the background. The electronic design of the monitor prevents the β-channel signal from being recorded, which makes it impossible to achieve the absolute measurement as with conventional coincidence methods. To ensure the accuracy of the monitoring results, the 18F aerosol monitor needs to be calibrated. In this paper the monitor was calibrated using a standard 18F surface source produced by the intermittent spot method on a filter paper base. By means of the decay of the 18F source and the automatic recording function of the monitor, the indication number of the monitor was measured at six source activities of different orders of magnitude, i.e. the count of the energy spectrum at the position of the 511 keV γ-window. The data were used to obtain the activity response and uncertainty of the monitor's readings and to verify the feasibility of such a calibration scheme on the monitor. Two standard point sources of 22Na and 68Ge/68Ga were also used to calibrate the monitor to verify the feasibility of using long-lived nuclides to make alternative sources for calibration. Sets of 511 keV γ-window count data from γ and coincidence channels were recorded to investigate the effect of the γ coincidence additive effect and different β emission spectra on the activity response, and to determine which long-lived positron nuclides should be used in the field calibration. Calibration with a standard 18F surface source shows that the monitor can give stable and reliable readings of this type of standard source over different orders of magnitude within the range. The extended uncertainty (k=2) of the displayed activity response is 6.0%, which meets manufacturing requirements. And the calibration of the long-lived positron nuclide standard point sources shows that the positron spectrum has a great influence on the activity response of the monitor, and the γ coincidence additive effect is not very significant. Therefore, it is recommended to use 22Na with β energy close to 18F to make point sources or surface sources for field calibration.

Continuous monitoring and assessment of satellite sensor radiometric response are crucial for timely detection of anomaly of the sensor performance, especially for operational optical sensors (e.g., FY-3D MERSI-II) beyond the expected lifespan. Improving calibration coefficients ensures the acquisition of high-precision and consistent observational Level 1 data records for long time-series researches. To evaluate the radiometric response stability of FY-3D MERSI-II in the reflective solar band, this study constructs parametric simplified and nonsimplified Warren bidirectional reflectance distribution function (BRDF) models using FY-3D MERSI-II nadir observation data at Dome C, Antarctica during the austral summer (October–February in next year) from 2019 to 2023. Subsequently, BRDF correction is applied to eliminate variations in the Dome C data caused by its non-Lambertian nature. The corrected data are analyzed for trends and compared with previous publications. The findings indicate that parameter simplification results in this study improve the calibration accuracy for band 3 (0.650 $\bm {\mu }\text{m}$) and band 4 (0.865 $\bm {\mu }\text{m}$) by 18.1% and 9.5%, respectively. Further analysis for the instrument degradation reveals that [the total multiyear degradation rate, average annual degradation rate] are within [$\pm$2.2%, $\pm$0.54%] and [$\pm$0.5%, $\pm$0.13%], respectively. Comparative validation results demonstrate good agreement with previous studies, showing a deviation of the average annual degradation rate between corresponding bands within $\pm$1%. It demonstrates that the stability of the MERSI-II is comparable to MODIS, which is one of the most frequently used medium-resolution sensors over last 25 years.

Future traffic contexts will likely involve the coexistence of human-driven vehicles and connected and automated vehicles (CAVs). To assess the impact of CAVs, especially in large-scale applications, intermediate hybrid multi-scale models can be used. These models are easily adaptable to traffic control strategies by employing disaggregated modeling in regions where such strategies are implemented and macroscopic modeling in other regions indirectly affected by the controlled infrastructure. This paper focuses on a model previously established in the literature, the H - CA&CTM (Hybrid Cellular Automata -CA- Cell Transmission Model-CTM), with an emphasis on the micro model that can be implemented in the hybrid traffic flow model. The research has two primary aims: 1) Investigate the calibration of the CA model with respect to various cell lengths using two distinct approaches: simulating all vehicles together in a closed ring layout and simulating each vehicle using data obtained from its respective follower; 2) Utilize vehicle trajectory data for the calibration procedure, enabling a comprehensive comparison of methods. Two detailed approaches were considered: 1. Measured Leader – Simulated Follower interaction approach. 2. Simulated Leader – Simulated Follower interaction approach. The major finding of the paper is that the calibrated parameters obtained using the Simulated Leader approach display greater regularity across different cell lengths.

Hydrological modeling is a critical and decisive technique for estimating hydrological processes and the availability of water resources. In our research, we used Hydrologic Modeling System (HEC-HMS), a 4.4 version, to achieve the affected goals, and a watershed in the east of Algeria, known as Rhumel Smedou watershed, was chosen as a case study which covers an area of 1,083 km2. A Geographic Information System (GIS) was used to analyze and process the Digital Elevation Model 30-meter resolution data accompanied with Landsat of Land Use Land Cover 10-meter resolution with accuracy to extract and deduce the basic inputs of the simulation model, which are: curve number (%), lag time (mn), impermeability (%), and rainfall (Ia) (mm). The research purpose is to compute runoff depth and volume rate for four extreme events. Two events are chosen for model calibration and two for model validation. The results of the statistical tests [NSE (%) RMSE_PBIAS (%)] proved the success and ability of the model to achieve the research objectives, and their averages were as follows: [82%_0.45_32.55%] for calibration phase, and [77%_ 0.45_30.95%] for validation phase. After calibrating and validating the effectiveness of the model, the volume and flow were predicted for different return periods (2y, 10y, 50y, and 100y).

Currently, blood pressure assessment is on the verge of shifting from classical cuff-based measurements to continuous estimation using smart devices based on the absorption and reflection characteristics of light in tissue. This type of blood pressure estimation has been known for a long time and depends on calibration with conventional blood pressure measurement systems. Products from well-known manufacturers in this market already perform calibration with automatic cuffs using an oscillometric estimation. The main aim is to show that classical oscillometric blood pressure estimation is not suitable for calibration, which allows a burden-free and continuous estimation by today’s approaches. This study identifies the reason and also the solution for this in the second-order blood pressure variation. The current approach to oscillometric estimation of blood pressure has an uncorrectable systematic error of the order of the second-order blood pressure variation. Therefore the current method has too much error to be used as a basis for calibration to allow continuous estimation of blood pressure based on another vital parameter. It is shown that when using a measurement of the second-order blood pressure variation as basis for such calibration this problem can be solved.