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ABSTRACTFor decades researches have been conducted in Ethiopia to improve wheat productivity and quality. This involved extensive selection of breeding lines based on agronomic and laboratory data from numerous trials. However, generating laboratory data for all these services was both time-consuming and expensive. Hence, the study aimed to develop a very rapid and cost-effective testing method for wheat genotypes and varieties using near-infrared spectroscopy. For this purpose, we collected 155 bread wheat samples from high-growing potential areas of Sinana, Kulumsa, and Holeta. The wet chemistry analysis was done in duplicate following the international standard official methods. The same samples were also scanned using TANGO’S Bruker NIRS machine in duplicate to acquire spectral data. A calibration model was then developed by fitting the reference and spectral data using the partial least square (PLS). The calibration models exhibited a high level of strength for all parameters. The performance of the calibration model for protein (R2 = 0.99; RPD = 9.63), moisture (R2 = 0.96; RPD = 5.29), ash (R2 = 0.98; RPD = 6.39), total gluten (R2 = 0.98; RPD = 8.09), dry gluten (R2 = 0.97; RPD = 5.57), gluten index (R2 = 0.96; RPD = 5.23), and falling number (R2 = 0.97 and RPD = 6.23) were deemed suitable for wheat process and quality control purposes. But the models for moisture and gluten index (R2 = 0.96) were found to be suitable for screening and simple quality control purposes only. Therefore, this study clearly demonstrated that the near-infrared spectroscopy model is a highly reliable and robust method for predicting the key quality traits of bread wheat grains, serving the needs of both breeding programs and industry raw material control.

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.

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.

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).

ABSTRACTComputational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor – host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models’ validation and provide an ambitious outlook toward rigorous and reliable calibration.

Land use changes are one of the main factors in the amount of surface runoff changes in watersheds. Therefore, it is necessary to investigate it to reduce the damages (human and financial) caused by floods and to modify watershed management. The watershed of Nahre Azam is located in the north of Shiraz city and a lot of loss of life and money to the residents of Shiraz due to floods has occurred in previous years. The present research was conducted to investigate the relationship between land use change and runoff in the Nahre Azam watershed in Shiraz using the SWAT model in the period of 2004-2020. The model was calibrated using data from 2004 to 2014 and validated for 2015 to 2020. These images were classified into 6 main land uses using the supervised classification method after performing necessary pre-processing, and a land use map was prepared for 2040 using the Markov chain method. Then, the effect of the land use change in 2003 and 2040 on the amount of simulated runoff was evaluated with the recalibrated model. The calibration results of Nahre Azam watershed for the values of statistical parameters in the calibration step for the coefficient of determination, P-Facor and R-Facor are 0.77, 0.72, and 2.43, respectively, and for the validation step we obtained 0.69, 0.65, and 2.3 respectively. The analysis of the land use map showed that the main land use change in the region related to the conversion of pastures to agricultural land and urban land, which caused a decrease in pastures. Also, the results of the model simulation using the land use maps of 2003 and 2040 indicated that the amount of runoff decreased. The results revealed that if all the uncertainties are minimized, the calibrated SWAT model can produce acceptable hydrological simulation results for the user, which is useful for water resource and environmental managers and politicians as well as city managers of Shiraz.

The Water Erosion Prediction Project (WEPP) model has been widely used for estimating runoff and soil loss. The evaluation of the latest version (version 2021.133) under a range of environmental conditions can provide confidence to its users. The objectives of this study were to evaluate the WEPP model for runoff and soil loss predictions using 1159 plot years of rainfall-runoff events data from field experimental plots with various climates, soils, topographies, and crops. WEPP runoff and soil loss predictions were compared to the observations before and after input parameter calibration. The results showed good predictions of runoff and soil loss were obtained with both the uncalibrated and calibrated WEPP model for all considered scales with Nash-Sutcliffe model efficiencies (NSE) over 0.4. The calibration of input baseline effective hydraulic conductivity (ke), baseline critical shear stress (τc), baseline rill erodibility (kr), and baseline interrill erodibility (ki) improved WEPP model performance with NSE values of 0.98 and 0.91 for average annual runoff and soil loss predictions, respectively. The WEPP model tended to underestimate the runoff and soil loss for large events with runoff over 100 mm and soil loss over 120 t/ha. Good event runoff and soil loss predictions (NSE ≥0.4) were obtained for the most common cropping/management systems considered, including corn, cotton, tilled fallow, and wheat after calibration. This study illustrates the most recent WEPP model's performance for runoff and soil loss predictions, and provides a comprehensive set of results.

AbstractThe current study analyzed the impact of Land Use Land Cover (LULC) change on streamflow and surface water availability in the Akaki catchment of the Awash Basin, Ethiopia. Soil and Water Assessment Tool (SWAT) and Climatic Water Balance (CWB) models were used. The SWAT model calibrated and validated the daily streamflow. The results indicated that from 1993 to 2016, built-up and barren land areas increased by 5.3% and 3.4%, respectively. The SWAT model over-simulated the peak flows and best simulated the medium and low flows. Based on the calibrated daily streamflow, the runoff was 236.01 mm and 272.59 mm under the 1993 and 2016 LULC scenarios. The total water yield of the catchment was 366.7 mm and 382.01 mm for the 1993 and 2016 LULC scenarios, respectively. The seasonal CWB of the catchment depicted decreasing trend except for the Tsedey (SON) season. Based on the current study’s findings, since LULC change enhanced surface runoff and total water yield in the catchment, proactive flood management is recommended to reduce the impact of the flood hazard on life and property.

Optical satellite data products (e.g., Sentinel-2, PlanetScope, Landsat) require proper validation across diverse ecosystems. This has conventionally been achieved using airborne and more recently unmanned aerial vehicle (UAV) based hyperspectral sensors which constrain operations by both their cost and complexity of use. The MicaSense Altum is an accessible multispectral sensor that integrates a radiometric thermal camera with 5 bands (475 nm–840 nm). In this work we assess the spectral reflectance accuracy of a UAV-mounted MicaSense Altum at 25, 50, 75, and 100 m AGL flight altitudes using the manufacturer provided panel-based reflectance conversion technique for atmospheric correction at the Mer Bleue peatland supersite near Ottawa, Canada. Altum derived spectral reflectance was evaluated through comparison of measurements of six known nominal reflectance calibration panels to in situ spectroradiometer and hyperspectral UAV reflectance products. We found that the Altum sensor saturates in the 475 nm band viewing the 18% reflectance panel, and for all brighter panels for the 475, 560, and 668 nm bands. The Altum was assessed against pre-classified hummock-hollow-lawn microtopographic features using band level pair-wise comparisons and common vegetation indices to investigate the sensor’s viability as a validation tool of PlanetScope Dove 8 band and Sentinel-2A satellite products. We conclude that the use of the Altum needs careful consideration, and its field deployment and reflectance output does not meet the necessary cal/val requirements in the peatland site.

The research aimed to evaluate the possibility of applying the HBV model for assessing the flow of the Ile Alatau Mountain Range rivers. The main part of the corresponding water resources forms on the northern slopes of the Ile Alatau making them a significant water balance and water supply factor for the major cities of Almaty, Kaskelen, Talgar, and Yesik. The article includes a brief description of the HBV model, as well as hydrometeorologicaland topographical inputs, and continues with the description of the flow simulation outputs for the rivers with the catchment area varying between 71-601 km2. For the periods of 2000-2016, the model parameters were calibrated using the GAP optimization algorithm. The model’s performance was evaluated based on several criteria: Nash-Sutcliffe Efficiency (NSE), Percent bias (PBIAS), and Root Mean Standard Deviation Ratio (RSR). The selection of optimal parameters allowed obtaining the following model efficiency values: 0.80-0.93 (as per NSE), -0.78 to -15.33% (as per PBIAS), and 0.27–0.80 (as per RSR). The calculated model efficiencies point to the sufficient correlation between the dynamics of the observed and simulated runoffs during the calibration period. The study likewise included the assessment of the HBV model applicability as a forecasting technique based on the ratio. The outcomes of that exercise confirmed the facility of using the model for predicting the runoff of the Kaskelen, Talgar, Ulken Almaty, and Kishi Almaty Rivers. Due to the fact that the observation data were available only for 2020, the HBV model parameters of all the studied rivers, except the Talgar River, underwent validation for the periods of 2017-2020. The calibrated and validated parameters of the obtained Ile Alatau Mountain River Model can be recommended for runoff modeling based on the HBV-light model as well as for runoff forecasting, namely for short- and medium-term water flow forecasts, with the exception of the Turgen, Prokhodnaya and Uzyn Kargaly River Basins.

Abstract The expected growth in wind energy capacity requires efficient and accurate models for wind farm layout optimization, control, and annual energy predictions. Although analytical wake models are widely used for these applications, several model components must be better understood to improve their accuracy. To this end, we propose a Bayesian uncertainty quantification framework for physics‐guided data‐driven model enhancement. The framework incorporates turbulence‐related aleatoric uncertainty in historical wind farm data, epistemic uncertainty in the empirical parameters, and systematic uncertainty due to unmodeled physics. We apply the framework to the wake expansion parametrization in the Gaussian wake model and employ historical power data of the Westermost Rough Offshore Wind Farm. We find that the framework successfully distinguishes the three sources of uncertainty in the joint posterior distribution of the parameters. On the one hand, the framework allows for wake model calibration by selecting the maximum a posteriori estimators for the empirical parameters. On the other hand, it facilitates model validation by separating the measurement error and the model error distribution. In addition, the model adequacy and the effect of unmodeled physics are assessable via the posterior parameter uncertainty and correlations. Consequently, we believe that the Bayesian uncertainty quantification framework can be used to calibrate and validate existing and upcoming physics‐guided models.

The ECOSTRESS push-whisk thermal radiometer on the International Space Station provides the highest spatial resolution temperature retrievals over the ocean that are currently available. It is a precursor to the future TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA) 50 to 70 m scale missions. Radiance transfer simulations and triple collocations with in situ ocean observations and NOAA L2P geostationary satellite ocean temperature retrievals were used to characterize brightness temperature biases and their sources in ECOSTRESS Collection 1 (software Build 6) data for the period 12 January 2019 to 31 October 2022. Radiometric noise, non-uniformities in the focal plane array, and black body temperature dynamics were characterized in ocean scenes using L1A raw instrument data, L1B calibrated radiances, and L2 skin temperatures. The mean brightness temperature biases were −1.74, −1.45, and −1.77 K relative to radiance transfer simulations in the 8.78, 10.49, and 12.09 µm wavelength bands, respectively, and skin temperatures had a −1.07 K bias relative to in situ observations. Cross-track noise levels range from 60 to 600 mK and vary systematically along the focal plane array and as a function of wavelength band and scene temperature. Overall, radiometric uncertainty is most strongly influenced by cross-track noise levels and focal plane non-uniformity. Production of an ECOSTRESS sea surface temperature product that meets the requirements of the SST community will require calibration methods that reduce the biases, noise levels, and focal plane non-uniformities.

High-resolution TIR data are essential for a wide range of studies, e.g., in environmental monitoring, urban effects, water stress, agricultural productivity, and land/water resources. Landsat series provides high-resolution TIR data, but data accuracy must be ensured to contribute to these fields. After the calibration problems of Landsat 8 (L8) TIRS due to a stray light effect, efforts were made in the design of Landsat 9 (L9) TIRS-2 to mitigate this effect. In any case, field calibration and validation activities are necessary to evaluate the accuracy of recently launched sensors. These activities require high-quality ground TIR radiance data that are representative at satellite spatial resolution and that quantify spatial–temporal fluctuations at the experimental sites concurrently to satellite overpasses. However, these requirements are not always accomplished in the literature.This paper provides vicarious calibrations of the L9 TIRS-2 data with accurate ground measurements acquired using multi-instrument spatial and temporal sampling in a rice paddy area from December 2021 to August 2023, in order to contribute to the calibration and validation of L9 TIRS-data.In March 1, 2023 L9 TIRS-2 scenes started to be reprocessed and calibration was updated. Both the original calibration and that after reprocessing were evaluated. Close results were obtained for band 10 (b10). When comparing satellite brightness temperatures and those determined from in situ LSTs, systematic differences of −0.3 K were shown, with root-mean-square differences (RMSDs) of 0.5 K. Results for band 11 showed a bias of −0.1 K for the original data and of 0.2 K after the reprocessing, with RMSDs of 0.6 K.The L2 LST product generated operationally from b10 data was also evaluated, together with alternative single-channel (SC) algorithms. A bias (RMSD) of 0.4 K (1.1 K) was obtained for the product. However, negative biases were shown both with the radiative transfer equation used on b10 radiances and the alternative SC algorithm.Finally, we validated six types of split-window (SW) algorithms applied to L9 TIRS-2 data before and after the reprocessing. A systematic difference of about −0.6 K between SW LST biases when using the reprocessed and original data was observed. In any case, for most of the algorithms with original and reprocessed data, LST biases and standard deviations were within the recommended threshold of 1 K, which proves the good performance of L9 TIRS-2 data to monitor LST at high spatial resolution.

Cuff-based home blood pressure (BP) devices, which have been the standard for BP monitoring for decades, are limited by physical discomfort, convenience, and their ability to capture BP variability and patterns between intermittent readings. In recent years, cuffless BP devices, which do not require cuff inflation around a limb, have entered the market, offering the promise of continuous beat-to-beat measurement of BP. These devices take advantage of a variety of principles to determine BP, including (1) pulse arrival time, (2) pulse transit time, (3) pulse wave analysis, (4) volume clamping, and (5) applanation tonometry. Because BP is calculated indirectly, these devices require calibration with cuff-based devices at regular intervals. Unfortunately, the pace of regulation of these devices has failed to match the speed of innovation and direct availability to patient consumers. There is an urgent need to develop a consensus on standards by which cuffless BP devices can be tested for accuracy. In this narrative review, we describe the landscape of cuffless BP devices, summarize the current status of validation protocols, and provide recommendations for an ideal validation process for these devices.

Abstract Background Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models. Methods We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers. Results Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models. Conclusion Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.

Nzoia River Basin is an important water resources development and management unit not only in Kenya but also in the region. The basin drains into Lake Victoria, frequently experiences severe flooding, and the catchment area has recently been dominated by unsustainable human activities. The resulting perturbations coupled with a changing climate could have far-reaching consequences in the near future. Efforts to understand the influence of the changes are complicated by a dwindling hydro-meteorological network. This study explored the efficacy of satellite data to estimate run-off time series for the basin. NAM model in Mike 11 was set-up, calibrated and validated. A run-off time series was then generated. The Goddard Earth Observing System model version 5 (GEOS-5) satellite data from NASA was then used to derive run-off series when calibrated and when not. The results were compared to identify which approach resulted to better estimates. The optimal runoff model parameters observed were 12.7 for Umax, 136 for Lmax, 0.2 for CQOF, 1100 for CKIF, 61.65 for CK1, 2, 0.248 for TOF, 0.198 for TIF, 0.0495 for TG and 2825 for CKBF. These gave a water balance of about 0.98 and root mean square error of about 460 with R2 of 0.9 and 0.8 for calibration and validation, respectively. Calibrated GEOS-5 data performed reasonably well in runoff estimation for Nzoia basin. While non-calibrated satellite data was poor with an r-squared of 0.6 in relation to observed discharge, calibrated data performed way better with an r-squared of 0.96 for daily values. Satellite-derived runoff was found to overestimate the discharge especially the peak discharges. This study showed that satellite data such as NASA's GEOS-5 when adequately calibrated provide suitable estimates for discharges in basins that are sparsely gauged. HIGHLIGHTS Nzoia River Basin is a critical water resources development and management unit but useful decisions cannot be made since the hydro-meteorological network is declining.; Satellite data has been explored to access its efficacy in runoff generation.; Surface-groundwater interaction is the main aspect that influences runoff generation.; Better estimates were derived from calibrated GEOS-5.;

The experiments were conducted at Agro Climate Research Centre, TNAU, Coimbatore. Calibration and validation of AquaCrop model was done using Winter and Kharif, 2022 data. Calibration showed that AquaCrop accurately simulated the canopy cover by low RMSE≤13.1%, good E≤0.76, high d≤0.94 and high R2 values ≥0.98 and biomass development by low RMSE≤13.2%, high E≤0.92, good d≤0.68 and high R2 values ≥0.95. During calibration, model well-simulated the CC under second sowing (D2) and biomass under third sowing (D3). Validation showed almost good fit of CC by low RMSE≤22.0%, good E≤0.68, high d≤0.84 and high R2 values ≥0.97 and biomass development with low RMSE≤7.1%, good E≤0.66, good d≤0.60 and high R2 values ≥0.98. During Validation, model well-simulated the CC and biomass under third sowing (D3). Model showed good fit of yield during first sowing window (D1) with a less deviation for both calibration and validation (15.6% and 5.8% respectively). From the result it could be concluded that sowing windows influence on baby corn production was accurately simulated using AquaCrop during calibration (R2=0.94) and validation (R2=0.98). Hence, AquaCrop proved to be a feasible tool for maximizing the Baby corn yield under different sowing windows.

In recent years, the concept of a Fiducial Reference Measurement (FRM) has been developed to highlight the need for precise and well-characterised measurements tailored explicitly to the post-launch calibration and validation (Cal/Val) of Earth observation satellite missions. The confidence that stems from robust, unambiguous uncertainty assessment of space observations is fundamental to assessing the changes in the Earth system and climate model prediction and delivering the essential evidence-based input for policy makers and society striving to mitigate and adapt to climate change. The underlying concept of an FRM has long been a core element of a Cal/Val program, providing a ‘trustable’ reference against which performance can be anchored or assessed. The ‘FRM’ label was created to embody into such a reference a set of key criteria. These criteria included the establishment of documented evidence of uncertainty with respect to a community-agreed reference (ideally SI-traceable) and specific tailoring to the needs of a satellite mission. It therefore facilitates comparison and interoperability between products and missions in a cost-efficient manner. Committee on Earth Observation Satellites (CEOS) Working Group Cal/Val (WGCV) is now putting in place a framework to assess the maturity and compliance of a ‘Cal/Val reference measurement’ in terms of a set of community-agreed criteria which define it to be of CEOS-FRM quality. The assessment process is based on a maturity matrix that provides a visual assessment of the state of any FRM against each of a set of given criteria, making visible where it is mature and where evolution and effort are still needed. This paper provides the overarching definition of what constitutes an FRM and introduces the new CEOS-FRM assessment framework.

This paper proposes a preliminary calibration and validation of a macroscopic traffic flow model for signalised junctions. In fact, on the network signal setting design problem, a reliable modelling approach must be adopted to acknowledge the traffic flow effects, considering two phenomena: queue dispersion and spillback. The proposed model is an extension of the space-time discrete Cell Transmission Model (CTM), which can simulate dispersion and horizontal queue. This preliminary calibration and validation use real-world data collected on an arterial of the city of Salerno (south of Italy). Results showed that the estimated parameters are consistent with the literature.

This study was conducted in the Atwood and Tappan Lakes watersheds of the Tuscarawas basin of Ohio. The flow, total nitrogen (TN), and total phosphorus (TP) loadings were monitored with the help of local stakeholders for a few years at various locations of the watershed to develop the Soil and Water Assessment Tool (SWAT). The multi-site SWAT model calibration and validation were accomplished with a reasonable model performance. In the next step, the scenario analysis was conducted in the SWAT model using various BMPs, including vegetative filter strips, grass waterways, fertilizer reduction, crop rotation, and cover crops to evaluate their performance in reducing TN and TP from the watershed. While BMPS in many studies are decided based on researchers’ intuition, these BMPs were selected based on active consultation with the local stakeholders, who were engaged in the reduction of TN and TP loadings from the watersheds. Since the SWAT model calibration for TN and TP was not as good as the hydrologic model calibration, various scenarios of TN and TP reduction using BMPs were investigated for several years using both calibrated and uncalibrated SWAT models. We examined all the BMPs in 12 sub-watersheds of the Atwood and 10 sub-watersheds of the Tappan Lake watershed. The analysis indicated that the management practices of cover crops (rye) in combination with grass waterways with a 10% fertilizer reduction could minimize the TN and TP loading by as much as 88%, without significantly compromising the agricultural yield. However, a 10% fertilizer reduction without any BMPs could reduce TN and TP by just 9%. The cover crop (rye) including 10% fertilizer reduction with grass waterways seemed to be the most effective in reducing TN and TP, whereas the implementation of a filter strip led to a 70% reduction and was the next effective BMPs in reducing TN and TP loadings. In general, TN losses were reduced by 8% to 53%, while TP losses were reduced by 7% to 88%, depending on the BMPs used. By and large, the TN and TP reduction achieved through the calibrated model was not significantly different from the uncalibrated model, even though the reduction using the calibrated model was slightly higher for all scenarios than that of the uncalibrated model. The TN and TP loadings were highly sensitive to cattle grazing. When just 50% of the cattle were permitted to graze, the model predicted that there would be a 40% increase in total nitrogen and a 70% increase in total phosphorus in both watersheds. Our investigation revealed that monitoring the watershed at a small sub-watershed scale and calibrating the SWAT model for nitrogen and phosphorus is delicate.

Iran is located in the dry belt of the earth and is predicted to face water stress in the next half-century. Currently, the area of sugarcane cultivation in Khuzestan is over 85,000 hectares and due to the high water needs of sugarcane and drought conditions, optimization of water consumption and irrigation management is necessary to continue production. Therefore, in this study, the values of soil moisture, canopy cover, biomass yield in five treatments and irrigation levels (start of irrigation at 40%, 50%, 60%, 70%, and 80% soil moisture discharge) during 2 planting dates in the crop year 2015-2016 on sugarcane cultivar CP69-1062 in Amirkabir sugarcane cultivation and industry located in the south of Khuzestan was simulated by AquaCrop model. The measured data on the first culture date (D1) and the second culture date (D2) were used to calibrate and validate the model. The results of NRMSE statistics in canopy cover simulation in calibration and validation sets with values of 2.1 to 15.6% and 3.8 to 18.3%, respectively, and in biomass simulation with values of 6.2 to 15.2%, and 9.5 to 12.6%, respectively and coefficient of determination (R2), range 0.98 to 0.99 indicated that the high ability of the AquaCrop model in simulation canopy cover and biomass yield. whereas, the values of NRMSE of soil depth moisture in the calibration and validation sets ranged from 11.6 to 23.8, and 12.2 to 22.7, respectively, with a coefficient of determination (R2), 0.73 to 0.96 (calibration) 0.8 to 0.93 (validation) showed less accuracy of the model in the simulation. The best scenario is related to the third proposal that water consumption, water use efficiency, and yield are 1710 mm, 1.53, and 42.27 tons per hectare, respectively, which shows a reduction in water consumption of 360 mm.

The purpose of the research is to develop a methodology for the quantitative determination of fenbendazole (FBZ) and its metabolites in organs and tissues of sheep by high performance liquid chromatography tandem mass-spectrometry (HPLC-MS/MS).Materials and methods. The method for determining of FBZ and its metabolites after the administration of the supramolecular complex of fenbendazole was validated in accordance with international guidelines for the following indicators: linearity, recovery, specificity, precision, accuracy, limits of quantitative and qualitative determination.Results and discussion. The method of quantitative analysis of FBZ and its metabolites sulfone and sulfoxide in the body of sheep after the administration of the supramolecular complex has been adapted. The proposed method has a linear relationship (R > 0.99) in the range of 5–1000 ng / g and has shown a good reproducibility and accuracy.

The present study was conducted to examine the accuracy and applicability of the hydrological models Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center (HEC)- Hydrologic Modeling System (HMS) to simulate streamflows. Models combined with the ArcGIS interface have been used for hydrological study in the humid tropical Hemavathi catchment (5,427 square kilometer). The critical focus of the streamflow analysis was to determine the efficiency of the models when the models were calibrated and optimized using observed flows in the simulation of streamflows. Daily weather gauge stations data were used as inputs for the models from the 2014–2020 period. Other data inputs required to run the models included land use/land cover (LU/LC) classes resulting from remote sensing satellite imagery, soil map and digital elevation model (DEM). For evaluating the model performance and calibration, daily stream discharge from the catchment outlet data were used. For the SWAT model calibration, available water holding capacity by soil (SOL_AWC), curve number (CN) and soil evaporation compensation factor (ESCO) are identified as the sensitive parameters. Initial abstraction (Ia) and lag time (Tlag) are the significant parameters identified for the HEC-HMS model calibration. The models were subsequently adjusted by autocalibration for 2014–2017 to minimize the variations in simulated and observed streamflow values at the catchment outlet (Akkihebbal). The hydrological models were validated for the 2018–2020 period by using the calibrated models. For evaluating the simulating daily streamflows during calibration and validation phases, performances of the models were conducted by using the Nash-Sutcliffe model efficiency (NSE) and coefficient of determination (R2). The SWAT model yielded high R2 and NSE values of 0.85 and 0.82 for daily streamflow comparisons for the catchment outlet at the validation time, suggesting that the SWAT model showed relatively good results compared to the HEC-HMS model. Also, under modified LU/LC and ungauged streamflow conditions, the calibrated models can be later used to simulate streamflows for future predictions. Overall, the SWAT model seems to have done well in streamflow analysis for hydrological studies. HIGHLIGHTS The study's novelty is the comparative study of performances to simulate streamflow by using both HEC-HMS and SWAT hydrological models for the first time in the sub-humid tropical Hemavathi catchment.; The study's scope focuses on streamflow analysis to determine the efficiency of the models when the models were calibrated and optimized using observed flows in the simulation of streamflows.;

The self-sufficiency ratio and national average yield of wheat are low in Japan. Reducing the yield gap and receiving the government subsidy for grain quality are vital strategies for profitability. Elucidating optimum nitrogen application scheme is awaited to attain both higher yield and appropriate grain protein content (GPC) for wheat cultivation in Japan. Such decision support can be realized by integrating field experimental knowledge to crop growth models, although they have scarcely been utilized for wheat production in Japan. Therefore, the purposes of this study were to apply a widely used crop growth model (APSIM) to wheat growth on an Andosol in the Kanto region in Japan by calibration and validation. Selected model parameters of APSIM-wheat for phenology, leaf growth, and grain formation were readjusted based on the phenology and growth data of soft and hard wheat cultivars. Then the model was validated by using similar variables obtained in an independent experiment. For the simulation of the optimum sowing for winter wheat in the Kanto area (November), the root mean square error for grain yield was 23 and 48 g m−2 for Ayahikari and Yumeshiho varieties, respectively, and that for GPC was 1.9 and 1.4%. Thus, the overall model performance was acceptable for optimum sowing. However, grain yield and dry matter production were significantly overestimated when the data of late sowing groups were included. Therefore, further model improvement was suggested to add an algorithm to reduce the number of emerged plants under cold temperature in late sowing conditions.

Ride-sourcing services have become increasingly important in meeting people's mobility needs since their emergence. Compared to traditional street-hailing taxi services, ride-sourcing services significantly reduce the matching frictions in the markets by matching drivers and passengers with relatively distant distances through an online platform. Motivated by this new feature as well as the need for designing operating and regulating strategies, researchers have attempted to describe these innovative ride-sourcing markets through mathematical models, the core of which is the matching functions for characterizing matching frictions. Previous studies have developed a variety of matching functions for ride-sourcing markets, including perfect matching function, Cobb-Douglas type matching function, queuing models, and some physical models. However, less is known about the applicability and performance of these matching functions, that is, under what situations each of these matching functions well characterizes the real market. To address this issue, this paper makes one of the first attempts to calibrate, validate, and compare the prevailing matching functions in the literature, and ascertain the conditions of their applicability. In particular, we establish a simulator to simulate a total of 420 scenarios of the ride-sourcing market under different combinations of supply and demand. The key performance metrics, including the matching rate in the market, passengers' average matching time, passengers' average pick-up time, and passengers' average total waiting time, are utilized to test and compare seven widely used matching functions under various market scenarios.

This research study provides a new framework to optimize pedestrian simulation parameters required to replicate emergency evacuation conditions specifically observed in high rise buildings, passenger vehicles and transport terminals. The simulation experiments are performed with generalised social force model in VISWALK simulation environment. The data required for optimization are obtained by conducting several evacuation experiments. The results indicate that pedestrian behavioural aspects, such as the relaxation time, distance from the other pedestrian and force exerted by closest pedestrians, have a significant impact on total evacuation time. The outcomes are in consistence with field observations and the calibrated parameters would help in simulating future scenarios including building level simulations and further simulating emergency evacuation of passengers from transport terminals and vehicles.

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R2 = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

Calibration and validation represent crucial but often-overlooked ingredients in the successful application of discrete element method (DEM) simulations. Without rigorous calibration/validation protocols, the results of DEM simulations can be imprecise or even unphysical, yet all too often the methods used by practitioners are at best cursory, and at worst entirely absent. As the particle-handling industries show an increasing interest in DEM, it is vital that this issue be resolved lest a potentially powerful tool be written off by industry as unreliable. In this work, we provide a concise overview of contemporary methods used in the calibration and validation of DEM simulations of powder flows, providing practical insights into their strengths and weaknesses, and ideas for manners in which they may be improved and/or rendered more easily adoptable in the future.

The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, regulators ask for independent validation reports from companies who apply for the approval of their internal models. We analyze here various ways to enable management and regulators to gain confidence in the quality of models. It all starts by ensuring a good calibration of the risk models and the dependencies between the various risk drivers. Then, by applying stress tests to the model and various empirical analyses, in particular the probability integral transform, we can build a full and credible framework to validate risk models.

The soil moisture active passive (SMAP) microwave radiometer is a fully-polarimetric L-band radiometer flown on the SMAP satellite in a 6 AM /6 PM sun-synchronous orbit at 685-km altitude. After the SMAP L1B_TB data product version 4 was released in 2018, the radiometer has undergone further calibration and validation. The goal is to reduce the difference between antenna temperature of ascending and descending orbits during the eclipse, and to reduce the dips in the calibration drift over the cold sky (CS) during the eclipse seasons in 2017 and 2018. The postlaunch calibration algorithm has been revisited by retrieving all of the calibration parameters simultaneously with two different options for the hot calibration source (the global ocean, or the radiometer internal reference load). The performance of the two options are compared here. The option with the radiometer internal reference load has been chosen by the SMAP science team for data release version 5. In addition, a correction offset is applied to the input signal to account for offsets during the early-mission stages with the SMAP synthetic aperture radar transmitter operating alongside the radiometer.

This research and collaboration work aims at the calibration and validation (Cal/Val) of the Chinese HY-2B satellite altimeter based upon two permanent Cal/Val facilities: (1) the China Altimetry Calibration Cooperation Plan in Qingdao, Bohai Sea and the Wanshan islands, China and (2) the permanent facility for altimetry calibration established by the European Space Agency in Crete, Greece. The HY-2B satellite altimeter and its radiometer have been calibrated and monitored using uniform, standardized procedures, as well as protocols and best practices, and they also built upon trusted and indisputable reference standards at both Cal/Val infrastructures in Europe and China. The HY-2B altimeter is thus monitored in a coordinated, absolute, homogeneous, long-term and worldwide manner. Calibration of altimeters is accomplished by examining satellite observations in open seas against reference measurements. Comparisons are established through precise satellite positioning, water level observations, GPS buoys and reference models (geoid, mean dynamic topography, earth tides, troposphere and ionosphere), all defined at the Cal/Val sites. In this work, the final uncertainty for the altimeter bias will be attributed to several individual sources of uncertainty, coming from observations in water level, atmosphere, absolute positioning, reference surface models, transfer of heights from Cal/Val sites to satellite observations, etc. Through this project, the procedures, protocols and best practices, originally developed in the course of the ESA FRM4ALT project, are updated, upgraded and followed at both Cal/Val facilities in Europe and China. All in all, the HY-2B satellite altimeter observes the sea level quite well and within its specifications.

The growth of citizen science presents a valuable potential source of calibration and validation data for environmental remote sensing at greater spatial and temporal scales, and with greater cost efficiency than is achievable by professional in situ reference-data collection alone. However, the frequent mismatch between in situ data-quality requirements for remote-sensing-product development and current data quality assurance in citizen science presents a significant challenge if widespread use of these complementary data sources is to be achieved. To evaluate the scope of this challenge, we conducted a targeted literature review into the nature of data-quality issues faced by citizen-science projects for routine incorporation into terrestrial environmental-monitoring systems. From the literature, we identify the challenges and trade-offs to inform best-practice implementation of data quality assurance in citizen-science projects. To assist practitioners in implementing our findings, we grouped these themes by stage of citizen-science project: (1) program planning and design; (2) participant engagement; (3) data collection; and (4) data processing. As a final step, we used our findings as the basis to formulate guiding questions that can be used to inform decision making when choosing optimal data-quality-improvement and assurance strategies for use of citizen science in remote-sensing calibration and/or validation. Our aim is to enhance future development of citizen-science projects for use with remote sensing in environmental monitoring.

Calibration of the existing building simulation model is key to correctly evaluating the energy savings that are achievable through retrofit. However, calibration is a non-standard phase where different approaches can possibly lead to different models. In this study, an existing residential building is simulated in parallel by four research groups with different dynamic simulation tools. Manual/automatic methodologies and basic/detailed measurement data sets are used. The calibration is followed by a validation on two evaluation periods. Monitoring data concerning the windows opening by the occupants are used to analyze the calibration outcomes. It is found that for a good calibration of a model of a well-insulated building, the absence of data regarding the users’ behavior is more critical than uncertainty on the envelope properties. The automatic approach is more effective in managing the model complexity and reaching a better performing calibration, as the RMSE relative to indoor temperature reaches 0.3 °C compared to 0.4–0.5 °C. Yet, a calibrated model’s performance is often poor outside the calibration period (RMSE increases up to 10.8 times), and thus, the validation is crucial to discriminate among multiple solutions and to refine them, by improving the users’ behavior modeling.

We describe and validate a feature-based system for calculation of likelihood ratios from 3D digital images of fired cartridge cases. The system includes a database of 3D digital images of the bases of 10 cartridges fired per firearm from approximately 300 firearms of the same class (semi-automatic pistols that fire 9 mm diameter centre-fire Luger-type ammunition, and that have hemispherical firing pins and parallel breech-face marks). The images were captured using Evofinder®, an imaging system that is commonly used by operational forensic laboratories. A key component of the research reported is the comparison of different feature-extraction methods. Feature sets compared include those previously proposed in the literature, plus Zernike-moment based features. Comparisons are also made of using feature sets extracted from the firing-pin impression, from the breech-face region, and from the whole region of interest (firing-pin impression + breech-face region + flowback if present). Likelihood ratios are calculated using a statistical modelling pipeline that is standard in forensic voice comparison. Validation is conducted and results are assessed using validation procedures and validation metrics and graphics that are standard in forensic voice comparison.

The Soil and Water Assessment Model (SWAT) is a continuous and semi-distributed model. The SWAT model is capable of being connected to the Geographic Information System (GIS). Using a range of information such as basin physical information (soil, land use and slope) as well as meteorological information such as rainfall, temperature, wind, relative humidity and solar radiation, this model can monitor hydrological processes in catchments on a daily time scale., Monthly and yearly. Simulation of hydrological processes in watersheds is essential for predicting future events in those basins and identifying ways to deal with them. In this study, using SWAT hydrological model, the runoff outflow of Santeh hydrometric station in the watershed of Lake Urmia in Kurdistan province in Iran with an area of ​​5306 km2. Runoff simulation was performed over a seven-year statistical period 2007-2013. The first five years of this period 2007-2011 for model calibration using 21 different parameters and the final two years of 2011 and 2012 for validation using two statistical indices (NSE) and coefficient of determination (R2). it placed. According to the simulated hydrograph and the monthly runoff observations, the calculated statistical indices of SWAT model in the calibration period and in the monthly time scale validation period have acceptable results in the runoff simulation, with coefficient values ​​(NSE) and (R2) in the calibration period were 0.78 and 0.82 respectively and for the validation period 0.9 and 0.91 respectively.

In this paper the authors detail the possibilities of modelling of finite element method (FEM) of glued insulated rail joints which are applied in railway tracks with continuously welded rails (CWR). A lot of laboratory tests (static and dynamic 3-point bending tests, axial pulling tests) were executed on glued insulated rail joints, the specimens were related to three different rail profiles applied in Hungary: MÁV 48.5; 54E1 (UIC54), 60E1 (UIC60), respectively. The static bending tests with many bay length values were conducted, before and after dynamic (fatigue) tests. 2-D beam models were made in FEM software using semi-rigid hinge as the simplified connection of fishplated glued insulated rail joint. The FEM models were calibrated and then validated with the static vertical displacement values in the middle-bay position measured in laboratory. The model validation was conducted with two methods.

Advances in astronomy led to the demand for measuring the spectra of multiple night sky objects simultaneously. Some of these Multi-Object Spectrographs use robotic systems that position optical fibers in the focal plane of the observing telescope. These systems rely on precise fiber placement in order to collect the light spectra of faint stars and galaxies. Here, we present how to design, control, and operate micro SCARA-like robots to position optical fibers to micrometer precision. As an illustrative example, we show the design and performance results of the SDSS-V fiber positioner, which has been build for the Apache Point Observatory and the Las Campanas Observatory with 500 units for each telescope.

Intensive agricultural practice in Ethiopian highlands results in increasing rates of soil erosion and reservoir sedimentation. The estimation of sediment yield and prediction of the spatial distribution of soil erosion on the upper Megech reservoir catchment enables the local governments and policymakers to maximize the design span life of the Megech reservoir through implementing appropriate soil conservation practices. For this study, the sediment yield was estimated and analyzed through hydrological modeling (SWAT). The simulated outputs of the model show that the mean annual surface runoff was 282 mm and the mean annual streamflow was 153 m3/s. Similarly, 12.33 t/ha mean annual total sediment load gets into the Megech reservoir. The model performance standard used to evaluate the model result indicates that the model was superior in performing the trend of runoff and sediment yield in both calibration and validation periods. Finally, the most erosion vulnerable sub-basins that could have a significant impact on the sediment yield of the reservoir were identified. Based on this, sub-basin 7, 25, 27, 18 and 29 were found to be the most erosion sensitive areas that could have a significant contribution to the increment of sediment yield in the Megech reservoir. Considering the land use, soil type, slope, and relief of erosion vulnerable sub-basins cut off drains, fallow land, contour ploughing, Fanya juu terraces, soil bunds combined with trenches and trees could be the possible management strategies to reduce the sediment yield in the catchment.

Abstract Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. Methods We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. Results High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values

Heat flux sensors are focused related to energy conservation. They are convenient tools which can detect direction and amount of heat flux. It is necessary to determine sensitivity of heat flux sensors before they are used. The sensitivity is a relation coefficient between heat flow density and output voltage of sensor. The steady state method is used to determine it in many cases. National Metrology Institute of Japan (NMIJ) of National Institute of Advanced Industrial Science and Technology (AIST) is supplying calibration service for heat flux sensors using by a Guarded Hot Plate (GHP) apparatus. The measurement with GHP apparatus takes long time more than one week although it gives reliable result with small uncertainty. In order to solve this problem, the small apparatus for practical calibration of heat flux sensors was developed according to the steady state method. The apparatus has beautiful steady temperature gradient in the volume of 30 mm × 30 mm x 30 mm. The cover made by expanded polypropylene is effective to decrease heat loss. It takes about 40 minutes to be steady state. Uncertainty of sensitive coefficient estimation using this apparatus was roughly evaluated less than 10% with the coverage factor k = 2.

Barley is one of the top five grain crops in Ethiopia, covering approximately 1 million hectares, and is grown for food, animal's feed and industrial purposes. Ethiopian Agricultural Research Institute (EIAR) is also conducting a barley research and breeding program with the aim of improving grain yield and resilience through screening lines. Sample materials used for the experiment were taken from 2018, 2019 and 2020 seasons to capture the variation in the growing year. As a result, the spectral data of the samples and the wet chemical data were generated using official international methods. Subsequently, a recalibration based on 280 samples of breeding lines of different genotypes, growing years and locations was developed in order to predict the protein, extract and friability content characteristics of important barley malt parameters. Wet chemical data were collected from processed/malted barley grains. A calibration model was created on the basis of the calibration software OPUS LAB 7.5 from Bruker Optics GmbH. The protein calibration (R2c = 0.90; RPD = 3.8) can be viewed as broadly applicable; the extract content (R2c = 0.88 and RPD = 2.8) and friability (R2c = 0.85 and RPD = 2.7) could be useful as well as a good predictive model for line screening. The calibration model was missing to predict long-range sample sources before the model range was expanded by recalibration process including different sample sources. Therefore, these NIRS models enable the selection of suitable food and malt barley genotypes at wide range. Since NIRS is fast and inexpensive, the barley improvement program can increase selection intensity, especially in the early testing phase. So the best precision of the NIRS recalibration discovered in this study enables to identify top candidate lines with a minimum of errors.

Tidal sand waves form a dynamic bed pattern, widely occurring in shallow shelf seas such as the North Sea. Their importance to coastal engineering has inspired many advances in process-based sand wave modelling, aimed at explaining physical mechanisms in the formation stage (‘linear regime’) and capturing the finite amplitude evolution to equilibrium states (‘nonlinear regime’). However, systematic validation of particularly the nonlinear sand wave models is still lacking. Here, we perform a two-step calibration and validation study of a sand wave model (specifically, their linear and nonlinear model versions) against field data from the North Sea. In the first step, the linear model is calibrated by seeking overall values of two uncertain input parameters (slip parameter, wave period) for which the modeled and observed wavelengths show the best agreement. In the second step, using the calibrated input parameters and preferred wavelengths from the linear model, equilibrium heights from the nonlinear sand wave model are validated against the observed sand wave heights. Our results show satisfactory agreement between observed and modeled sand wave lengths (from the linear sand wave model) and a systematic overprediction of sand wave heights (using the nonlinear model). Regression analysis can be used to rescale the nonlinear model results to obtain realistic predictions of sand wave heights.

This study calibrated and compared the capabilities of hourly global horizontal irradiance (GHI) clear sky models for six Moroccan locations, using the McClear clear sky model as a reference. Complex clear sky models, namely Bird, Simplified Solis, Ineichen and Perez, and simple clear sky models, namely Adnot–Bourges–Campana–Gicquel (ABCG), Berger–Duffie, and Haurwitz were tested. The SOLCAST satellite-based dataset estimates were validated against the McClear clear sky model. pvlib python was used to configure the models, and ERA5 hourly fractional cloud cover was used to identify clear-sky days. The study period was from 2014 to 2021, and the study sites were in different climatic regions in Morocco. Bar graphs, tables, and quantitative statistical metrics, namely relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2), were used to quantify the skill of the clear sky model at different sites. The overall rMBE was negative in 5/6 sites, indicating consistent overestimation of GHI, and positive in Tantan (14.4%), indicating frequent underestimation of GHI. The overall rRMSE varied from 6 to 22%, suggesting strong agreement between clear sky models and the McClear clear sky model. The overall correlation was greater than 0.96, indicating a very strong relationship. Overall, the Bird clear sky model proved to be the most feasible. Complex clear sky models outperformed simple clear sky models. The SOLCAST satellite-based dataset and ERA5 cloud fraction information could well be used with quantifiable certainty as an accurate clear sky model in the study region and in other areas where complex clear sky models’ inputs are not available.

Discrete element modelling (DEM) parameters are of great importance for the accurate prediction of soil properties and disturbance. This study aimed to provide an efficient method for accurately determining the DEM parameters of a paddy soil with a high moisture content (45.66%). The DEM parameters of the paddy soil modelled using the Hertz–Mindlin with JKR contact model were determined by using the Plackett–Burman (PB), steepest ascent, and central composite tests. The accuracies of the developed DEM models were evaluated using actual slumping tests. Based on the PB test, the surface energy of soil, coefficients of soil–soil rolling friction, and coefficients of soil–steel static friction exerted larger influences on the overall relative error (δZH). The optimization results showed that to achieve a minimum δZH (5.96%), the surface energy of soil, the coefficients of soil–soil rolling friction, and the coefficients of soil–steel static friction should be 0.808 J m−2, 0.11, and 0.6, respectively. The optimized DEM model had an overall relative error of 7.27% with a coefficient of variation of 1.32%, indicating that the DEM parameters of the calibrated paddy soil had good accuracy.

Monitoring and tracking issues related to autonomous mobile robots are currently intensively debated in order to ensure a more fluent functionality in supply chain management. The interest arises from both theoretical and practical concerns about providing accurate information about the current and past position of systems involved in the logistics chain, based on specialized sensors and Global Positioning System (GPS). The localization demands are more challenging as the need to monitor the autonomous robot’s ongoing activities is more stringent indoors and benefit from accurate motion response, which requires calibration. This practical research study proposes an extended calibration approach for improving Omnidirectional Mobile Robot (OMR) motion response in the context of mechanical build imperfections (misalignment). A precise indoor positioning system is required to obtain accurate data for calculating the calibration parameters and validating the implementation response. An ultrasound-based commercial solution was considered for tracking the OMR, but the practical observed errors of the readily available position solutions requires special processing of the raw acquired measurements. The approach uses a multilateration technique based on the point-to-point distances measured between the mobile ultrasound beacon and a current subset of fixed (reference) beacons, in order to obtain an improved position estimation characterized by a confidence coefficient. Therefore, the proposed method managed to reduce the motion error by up to seven-times. Reference trajectories were generated, and robot motion response accuracy was evaluated using a Robot Operating System (ROS) node developed in Matlab-Simulink that was wireless interconnected with the other ROS nodes hosted on the robot navigation controller.

The AquaCrop model is one of the most recent models that can simulate the growth rates and yields of various crops based on water consumption levels. To determine the optimal irrigation level, data measured in two crop years (2018–2019) in different irrigation scenarios (full irrigation or 100% water requirement and 90, 80, 70, 60, and 50% irrigation) were calibrated and validated for arid and semiarid climatic conditions using the AquaCrop model. The model was calibrated to simulate the grain yields of winter wheat and barley using R2, RMSE, d, EF, and NRMSE statistical indicators. The obtained values of these indices were, respectively, 0.97, 3, 0.98, 0.94, and 4 for winter wheat and 0.98, 4, 0.92, 0.89, and 7 for barley. The model efficiency was also validated using crop harvest data in the crop year 2019. For grain yield simulation, the calculated values of R2, RMSE, d, EF, and NRMSE statistical indicators were, respectively, 0.99, 4, 0.97, 0.93, and 4.4 for winter wheat and 0.97, 7, 0.94, 0.91, and 9 for barley. The data of field and modeled samples were analyzed by analysis of variance (ANOVA) using the F-test, and significant results were obtained for both crops in all applied scenarios at the 95% level.

The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project has deployed 194 profiling floats equipped with biogeochemical (BGC) sensors, making it one of the largest contributors to global BGC-Argo. Post-deployment quality control (QC) of float-based oxygen, nitrate, and pH data is a crucial step in the processing and dissemination of such data, as in situ chemical sensors remain in early stages of development. In situ calibration of chemical sensors on profiling floats using atmospheric reanalysis and empirical algorithms can bring accuracy to within 3 μmol O2 kg–1, 0.5 μmol NO3– kg–1, and 0.007 pH units. Routine QC efforts utilizing these methods can be conducted manually through visual inspection of data to assess sensor drifts and offsets, but more automated processes are preferred to support the growing number of BGC floats and reduce subjectivity among delayed-mode operators. Here we present a methodology and accompanying software designed to easily visualize float data against select reference datasets and assess QC adjustments within a quantitative framework. The software is intended for global use and has been used successfully in the post-deployment calibration and QC of over 250 BGC floats, including all floats within the SOCCOM array. Results from validation of the proposed methodology are also presented which help to verify the quality of the data adjustments through time.

In most developing countries, biophysical data are scarce, which hinders evidence-based watershed planning and management. To use the scarce data for resource development applications, special techniques are required. Thus, the primary goal of this study was to estimate sediment yield and identify erosion hotspot areas of the Andasa watershed with limited sediment concentration records. The hydrological simulation used meteorological, hydrological, suspended sediment concentration, 12.5 m Digital Elevation Model (DEM), 250 m resolution African Soil Information Service (AfSIS) soil, and 30 m resolution land-cover data. Using the limited sediment concentration data, a sediment rating curve was developed to estimate the sediment yield from discharge. The physical-based Soil and Water Assessment Tool (SWAT) model was employed to simulate streamflow and sediment yield in a monthly time step. The result shows that SWAT predicted streamflow with a coefficient of determination (R2) of 0.88 and 0.81, Nash–Sutcliffe Efficiency (NSE) of 0.88 and 0.80, and percent of bias (PBIAS) of 6.4 and 9.9 during calibration and validation periods, respectively. Similarly, during calibration and validation, the model predicted the sediment yield with R2 of 0.79 and 0.71, NSE of 0.72 and 0.66, and PBIAS of 2.7 and −8.6, respectively. According to the calibrated model result in the period 1992–2020, the mean annual sediment yield of the watershed was estimated as 17.9 t ha−1yr−1. Spatially, around 22% of the Andassa watershed was severely eroded, and more than half of the watershed (55%) was moderately eroded. The remaining 23% of the watershed was free of erosion risk. Therefore, the findings suggests that applying the sediment rating curve equation, in conjunction with hydrological and sediment modeling, can be used to estimate sediment yield and identify erosion hotspot areas in data-scarce regions of the Upper Blue Nile Basin in particular, and the Ethiopian highlands in general with similar environmental settings.

The objective of the study was to configure the Hydrological Modeling System (HEC-HMS) in such a way that it could simulate all-important hydrological components (e.g., streamflow, soil moisture, snowmelt water, terrestrial water storage, baseflow, surface flow, and evapotranspiration) in the Three-River Headwater Region. However, the problem we faced was unsatisfactory simulations of these hydrological components, except streamflow. The main reason we found was the auto-calibration method of HEC-HMS because it generated irrational parameters, especially with the inclusion of Temperature Index Method and Soil Moisture Accounting (an advanced and complex loss method). Similar problems have been reported by different previous studies. To overcome these problems, we designed a comprehensive approach to estimate initial parameters and to calibrate the model manually in such a way that the model could simulate all the important hydrological components satisfactorily.

Currently, more and more emphasis is being placed on reducing energy consumption in buildings to reduce greenhouse gases in the atmosphere. Building performance simulation is very useful to predict energy demand and indoor environment quality. An indispensable element of the simulation is the validation and calibration of the model, which is an arduous process. The aim of the study was to present a four-level validation (using measurement results) and calibration of a thermal model of a naturally ventilated single-family house. Numerical calculations using co-simulation between EnergyPlus and Contam were performed. The results of the one-year simulation measurements of the indoor temperature and ventilation airflows were compared. After the calibration was performed, a high convergence of the results was found. The normalized mean bias error for hourly and monthly values did not exceed 1% and the coefficient of variation of the root mean squared error was a maximum of 7% with a simultaneous high correlation of the results in the range from 0.85 to 0.89. It was found that the final results were significantly influenced by the appropriate modeling of air exchange in the building, including the opening of windows.