Calibration Data Set Research Articles

An upgrade of MVC2, a MATLAB graphical user interface for second-order multivariate calibration: Beyond trilinear models

In the present report, an upgrade of a MATLAB graphical user interface (GUI) toolbox for implementing second-order multivariate calibration models is presented, named Multivariate Calibration 2 (MVC2). Due to the advances in the chemometric field, this software has undergone numerous changes since its last release in 2009. The new freely available MVC2 incorporates novel features and models that make it a very practical tool for data processing. Overall, the current version presents different types of algorithms, designed to provide specific degrees of model flexibility for different multi-way data scenarios: in addition to trilinear decomposition (TLD) and residual bilinearization (RBL) models, which were previously included in the 2009 version, parallel factor analysis 2 (PARAFAC2) and multivariate curve resolution (MCR) methods are now available. All these chemometric methodologies are integrated in the software for quantitative purposes, and if needed, they permit to exploit the so-called analytical second-order advantage. The program offers practical tools for data loading and visualization, model development and validation, and prediction in unknown samples. The software output also includes a comprehensive report on the analytical figures of merit (AFOMs) applicable to second-order calibration models. Specifically, a tool was incorporated for the diagnosis of the remaining rotational ambiguity in the MCR solutions. In the present report, the new features of MVC2 are illustrated through simulated examples and real calibration datasets.

Operationalizing the centiloid scale for [18F]florbetapir PET studies on PET/MRI.

The Centiloid scale aims to harmonize amyloid beta (Aβ) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI). We transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian-mixture-modelling-derived cutpoints for Aβ PET positivity were converted. The Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM-based Centiloids. Linear adjustment produced a WM-based cutpoint of 18.1. Transformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed. Centiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results.Centiloid values can be influenced by differences in acquisition.We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort.Whole cerebellum referenced values could be reliably transformed to Centiloids.White matter referenced values may be less generalizable between datasets.

Leveraging Google Earth Engine to estimate foliar C: N ratio in an African savannah rangeland using Sentinel 2 data

Rangelands are important fodder for livestock and wildlife, and provide a range of ecosystem services to the environment. Foliar nutrients such as nitrogen, carbon, and plant pigments such as chlorophyll can be used as indicators of rangeland stress, and play a vital role in determining their health and productivity. The C:N ratio is a key factor in regulating nutrient utilization efficiency and productivity in plants. Understanding the C:N ratio in rangelands could therefore help herders understand the nutrient limitations, and herbivores distribution to facilitate strategic grazing plans and management. Therefore, there is a need for spatially accurate and up-to-date information on C:N ratio to understand and monitor rangeland health for proactive rangeland management. Remote sensing approaches are spatially explicit, cost-effective, and efficient in monitoring foliar nutrient ratio in rangelands. Whereas, the new generation and advanced Sentinel 2 multispectral sensor has the potential to monitor vegetation health, the strength of its spectral settings in relation to predicting the C:N ratio in rangelands remains largely unexplored. Advanced and freely available Sentinel 2 multispectral sensor (MSI) with specialized red edge bands offer unprecedented opportunities in mapping and monitoring rangeland nutrients. Hence, this study examined the prospect of combined Sentinel-2 (MSI) spectral bands and vegetation indices, and the random forest algorithm to map the C: N ratio within a rangeland. To determine the C:N ratio distribution, the Random Forest and the Boruta variable selection were employed to assess the performance of the combined Sentinel 2 spectral bands and vegetation indices models. Results show an estimated accuracy R2 of 81 and 74, with RMSE of 2.38 and 2.68 for calibration and validation datasets of the C:N ratio model established by combining the spectral bands and vegetation indices. The random forest variable selection model indicates that the red edge bands, and near-infrared were the most valuable in predicting the C:N ratio. The red edge and near-infrared (Inverted Red-edge Chlorophyll Index) and near-infrared and red band (Enhanced Vegetation Index) vegetation indices were important predictor variables for estimating the C:N ratio. This study demonstrates the prospects and value of mapping the geographic distribution of the C:N ratio in rangelands using high spatial resolution Sentinel 2 MSI. This information will not only help determine nutrient deficiencies in rangelands but will also provide informed recommendation in mitigating landscape degeneration to allow for rangeland regeneration.

Spectral indices for tracing leaf water status with hyperspectral reflectance data

Plant water stress can be detected via remote sensing. The objective of the study was to determine which leaf water index is best for assessing leaf water content from the laboratory standpoint. This study investigated the relationship between equivalent water thicknesses (EWT), gravimetric water content (GWC), and plant water concentration in the 350- to 2500-nm reflectance spectral range. A total of 277 leaf samples taken from ten different plants were used as calibration dataset, and 605 leaves from different plants, including LOPEX93 and ANGERS database, were used for validation. Three specific indices were analyzed: simple ratio, normalized ratio, and double difference (Datt type of index). A regression approach based on the iteration method at 5-nm interval was used for model calibration. Three bands index was found the most suitable and was validated by 605 leaf samples: for the linear regression model, the index is ( R1910 − R1340 ) / ( R1910 − R1125 ) with R2 = 0.96 and root mean square error ( RMSE ) = 0.001 ( g / cm2 ) and, for nonlinear regression model the index is ( R1930 − R1425 ) / ( R1930 − R1360 ) with R2 = 0.95 and RMSE = 0.001 ( g/cm2 ) for EWT. The newly proposed indices take advantage of being able to eliminate additional noise created by the leaf surface, making them helpful for agricultural-related research.

Integration Vis-NIR Spectroscopy and Artificial Intelligence to Predict Some Soil Parameters in Arid Region: A Case Study of Wadi Elkobaneyya, South Egypt

Understanding and determining soil properties is reflected in improving farm management and crop production. Soil salinity, pH and calcium carbonate are among the factors affecting the soil’s physical and chemical properties. Hence, their estimation is very important for agricultural management, especially in arid regions (Wadi Elkobaneyya valley, located in the northwest of Aswan Governorate, Upper Egypt). The study objectives were to characterize and develop prediction models for soil salinity, pH and calcium carbonate (CaCO3) using integration soil analysis and spectral reflectance vis-NIR spectroscopy. To achieve the study objectives, three multivariate regression models: Partial Least Squares Regression (PLSR), Multivariate Adaptive Regression Splines (MARS) and Least Square-Support Vector Regression (LS-SVR)); and two machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN) were used. Ninety-six surface soil samples were collected from the study area at depths 0–5 cm. The data were divided into a calibration dataset (70% of the total) and a validation dataset (30% of the total dataset). The obtained results represent that the PLSR model was the best model for soil pH parameters where R2 of calibration and validation predictability = 0.68 and 0.52, respectively. The LS-SVR model was the best model to predict soil Electrical Conductivity (EC) and soil Calcium Carbonate (CaCO3) content, with R2 0.70 and 0.74 for calibration and R2 0.26 and 0.47 for validation, respectively. On the other hand, the results of the implemented machine learning algorithm model showed that RF was the best model to predict soil pH and CaCO3, as the R2 was 0.82 for calibration and 0.57 for validation, respectively. Nevertheless, the best model for predicting soil EC was ANN, with an R2 of 0.96 for calibration and 64 for validation. The results show the advantages of machine learning models for predicting soil EC, pH and CaCO3 by Vis-NIR spectroscopy. Therefore, Vis-NIR spectroscopy is considered faster and more cost-efficient and can be further used in environmental monitoring and precision farming.

Exploring the Potential of vis-NIR Spectroscopy as a Covariate in Soil Organic Matter Mapping

Robust soil organic matter (SOM) mapping is required by farms, but their generation requires a large number of samples to be chemically analyzed, which is cost prohibitive. Recently, research has shown that visible and near-infrared (vis-NIR) reflectance spectroscopy is a fast and accurate technique for estimating SOM in a cost-effective manner. However, few studies have focused on using vis-NIR spectroscopy as a covariate to improve the accuracy of spatial modeling. In this study, our objective was to compare the mapping accuracy from a spatial model using kriging methods with and without the covariate of vis-NIR spectroscopy. We split the 261 samples into a calibration set (104) for building the spectral predictive model, a test set for generating the vis-NIR augmented set from the prediction of the fitted spectral predictive model (131), and a validation set (26) for evaluating map accuracy. We used two datasets (235 samples) for Kriging: a laboratory-based dataset (Ld, observations from calibration and test datasets) and a laboratory-based dataset with vis-NIR augmented predictions (Au.p, observations from calibration and predictions from test dataset), a laboratory-based dataset with vis-NIR spectra as the covariance (Ld.co) and augmented dataset with predictions using vis-NIR with vis-NIR spectra for the covariance (Au.p.co). The first one to seven accumulated principal components of vis-NIR spectra were used as the covariates when we used the measurement of Ld.co and Au.p.co. The map accuracy was evaluated by the validation set for the four datasets using Kriging. The results indicated that adding vis-NIR spectra as covariates had great potential in improving the map accuracy using kriging, and much higher accuracies were observed for Ld.p.co (RMSE of 5.51 g kg−1) and Au.p.co (RMSE of 5.66 g kg−1) than without using vis-NIR spectra as covariates for Ld (RMSE of 7.12 g kg−1) and Au.p (RMSE of 7.69 g kg−1). With a similar model performance to Ld.p.co, Au.p.co can reduce the cost of laboratory analysis for 60% of soil samples, demonstrating its advantage in cost-efficiency for spatial modeling of soil information. Therefore, we conclude that vis-NIR spectra can be used as a cost-effective technique to obtain augmented data to improve fine-resolution spatial mapping of soil information.

Partial least square regression based machine learning models for soil organic carbon prediction using visible–near infrared spectroscopy

Monitoring and assessment of soil organic carbon (SOC) are critical for maintaining and enhancing the productivity of agricultural soils. The SOC is commonly determined through soil sampling and subsequent laboratory analysis using chemical methods. This method though very precise is time-consuming, labour-intensive and expensive. Contrarily, visible and near-infrared reflectance spectroscopy (VNIRS) may be utilised to estimate SOC in a quick, labour-saving, and cost-effective manner. In this study, 72 soil samples were collected for SOC estimation and spectra collection. This current work proposes to investigate the use of PLSR scores in place of raw spectral reflectance to increase both the computation and model efficiency by reducing the number of input variables while retaining the maximum information present in the original data. With the existing indices, ratio and normalized difference indices were calculated in all possible combinations and were regressed to SOC content to identify the best-performing indices. Ten different multivariate models were evaluated for SOC estimation using full-spectrum and partial least squares regression (PLSR) scores. The results revealed that reflectance gradually increased with increasing soil depth and decreasing SOC. The prediction models developed using existing indices were observed to be poor in predicting the SOC with the R2 values ranging from 0.009 to 0.34. The best spectral indices for estimating SOC were RI (R1888, R2015) and NDI (R1888, R2015) with R2 of 0.60, 0.61 and 0.39, 0.43 for calibration and validation datasets, respectively. The PLSR score-based multivariate models outperformed solo multivariate and optimized index-based models. Our study suggested that VNIRS with PLSR combined multivariate models can reliably be used for fast and non-invasive estimation of SOC.

Species distribution models predicting climate suitability for the psyllid Trioza erytreae, vector of citrus greening disease

The African citrus psyllid, Trioza erytreae (Hemiptera: Triozidae), is a vector of citrus greening (Huanglonbing - HLB), a bacterial citrus disease caused by Candidatus liberibacter spp. Native to Africa, T. erytreae was detected in the Canary Islands and Madeira in the early 2000s and then in northwestern Spain in 2014. Since then, T. erytreae has become established along the Atlantic coastal areas of the Iberian Peninsula. Therefore, an accurate assessment of the potential long-term establishment of T. erytreae in major citrus-growing regions of Europe and the world is urgently needed to design adapted control strategies. I calibrated correlative species distribution models to understand the bioclimatic characteristics that determine the distribution of T. erytreae, and to assess the climatic suitability of the world's major citrus-growing regions for the psyllid under current and future climate conditions. I calibrated the models using only distribution data from Africa (its native range), the Canary Islands, and Madeira, and evaluated them using available data from the invaded area in continental Europe. This approach aims to avoid spurious good measures of model accuracy arising from spatial autocorrelation between the calibration and evaluation datasets. The models identify mild summer and winter temperatures and high levels of precipitation as optimal conditions for long-term psyllid establishment, consistent with its physiology. In Europe, models predict only the Atlantic coastal regions of the Iberian Peninsula as highly climatically suitable, a spatial pattern that corresponds exactly to the area currently invaded by the psyllid. Models predict that most of the important citrus-growing areas in the world are, and will remain in the future, poorly adapted to T. erytreae except in case of future physiological adjustments. These results are crucial for the design of appropriate pest management strategies and are timely for Europe where the African citrus psyllid has recently been detected.

Validation and Recalibration of the Asbestos Removal Exposure Assessment Tool (AREAT).

The Asbestos Removal Exposure Assessment Tool (AREAT) was previously developed to estimate exposure to respirable asbestos fibres during abatement processes. The current study describes the validation and recalibration of the AREAT model with external data. During model validation, the AREAT model was expanded to be able to estimate asbestos exposure from an additional source category: 'unspecified asbestos remnants'. The validation dataset (n = 281) was derived from exposure measurement studies where for each exposure measurement the AREAT model parameters were coded and estimates were calculated. Pearson correlation coefficients (r) and intra class correlation coefficients (icc) were calculated as an indication of the agreement between the AREAT estimates and measured concentrations. In addition, the bias and the proportion of measurements with higher concentrations than model estimates were calculated. To expand and investigate model performance on exposure from 'unspecified asbestos remnants', a separate dataset was created with measurements collected during working with unspecified asbestos remnants, and similar validation comparisons were performed. Lastly, linear regression techniques were used to investigate possible improvements in model parameters. The model was recalibrated on a combined dataset consisting of the validation dataset and the original calibration dataset to increase model robustness. The validation comparisons showed good relative agreement (r) between AREAT estimates and measurements (r = 0.73) and a moderate absolute agreement (icc = 0.53). The overall relative bias was 108%, indicating an overall overestimation of exposure, and 4% of the estimated concentrations were higher than the actual measured concentrations. For the data subset concerning unspecified asbestos remnants, a moderate correlation between model estimates and measurement outcomes was found (r = 0.63). However, based on the low number of data in this subset, and moderate r, it was decided that cleaning of unspecified asbestos remnants is out of scope until more data are available. The results of this validation study suggested that two input parameters (product type friable material, efficacy of control measure foam) underestimated exposure. The effects of these parameters were updated to improve model performance. Compared to the original model, the recalibrated model resulted in slightly higher explained variance (62% compared to 56%) and lower uncertainty (15 compared to 17.3). The original AREAT model provided reliable asbestos exposure estimates with a sufficient level of conservatism taking into account the 90-percentile estimates. The model was further improved via the addition of a new feature and recalibration to predict asbestos exposure during the clean-up of unspecified asbestos remnants.

High-Resolution Mapping of Soil Organic Matter at the Field Scale Using UAV Hyperspectral Images with a Small Calibration Dataset

The rapid acquisition of high-resolution spatial distribution of soil organic matter (SOM) at the field scale is essential for precision agriculture. The UAV imaging hyperspectral technology, with its high spatial resolution and timeliness, can fill the research gap between ground-based monitoring and remote sensing. This study aimed to test the feasibility of using UAV hyperspectral data (400–1000 nm) with a small-sized calibration sample set for mapping SOM at a 1 m resolution in typical low-relief black soil areas of Northeast China. The experiment was conducted in an approximately 20 ha field. For calibration, 20 samples were collected using a 100 × 100 m grid sampling strategy, while 20 samples were randomly collected for independent validation. UAV captured hyperspectral images with a spatial resolution of 0.05 × 0.05 m. The extracted spectra within every 1 × 1 m were then averaged to represent the spectra of that grid; this procedure was also performed across the whole field. Upon applying various spectral pretreatments, including absorbance conversion, multiple scattering correction, Savitzky–Golay smoothing filtering, and first-order differentiation, the absolute maximum values of the correlation coefficients of the spectra for SOM increased from 0.41 to 0.58. Importance analysis from the optimal random forest (RF) model showed that the characterized bands of SOM were located in the 450–600 and 750–900 nm regions. When the RF model was used, the UAV hyperspectra data (UAV-RF) were able to successfully predict SOM, with an R2 of 0.53 and RMSE of 1.48 g kg−1. The prediction accuracy was then compared with that obtained using ordinary kriging (OK) and the RF model based on proximal sensing (PS-RF) with the same number of calibration samples. However, the OK method failed to predict the SOM accuracy (RMSE = 2.17 g kg−1; R2 = 0.02) due to a low sampling density. The semi-covariance function was unable to describe the spatial variability of SOM effectively. When the sampling density was increased to 50 × 50 m, OK successfully predicted SOM, with RMSE = 1.37 g kg−1 and R2 = 0.59, and its results were comparable to those of UAV-RF. The prediction accuracy of PS-RF was generally consistent with that of UAV-RF, with RMSE values of 1.41 g kg−1 and 1.48 g kg−1 and R2 values of 0.57 and 0.53, respectively, which indicated that SOM prediction based on UAV-RF is feasible. Additionally, compared with the PS platforms, the UAV hyperspectral technology could simultaneously provide spectral information of tens or even hundreds of continuous bands and spatial information at the same time. This study provides a reference for further research and development of UAV hyperspectral techniques for fine-scale SOM mapping using a small number of samples.

Parameter inference in a computational model of haemodynamics in pulmonary hypertension.

Pulmonary hypertension (PH), defined by a mean pulmonary arterial pressure (mPAP) greater than 20 mmHg, is characterized by increased pulmonary vascular resistance and decreased pulmonary arterial compliance. There are few measurable biomarkers of PH progression, but a conclusive diagnosis of the disease requires invasive right heart catheterization (RHC). Patient-specific cardiovascular systems-level computational models provide a potential non-invasive tool for determining additional indicators of disease severity. Using computational modelling, this study quantifies physiological parameters indicative of disease severity in nine PH patients. The model includes all four heart chambers, the pulmonary and systemic circulations. We consider two sets of calibration data: static (systolic and diastolic values) RHC data and a combination of static and continuous, time-series waveform data. We determine a subset of identifiable parameters for model calibration using sensitivity analyses and multi-start inference and perform posterior uncertainty quantification. Results show that additional waveform data enables accurate calibration of the right atrial reservoir and pump function across the PH cohort. Model outcomes, including stroke work and pulmonary resistance-compliance relations, reflect typical right heart dynamics in PH phenotypes. Lastly, we show that estimated parameters agree with previous, non-modelling studies, supporting this type of analysis in translational PH research.

Temperature resolved decay time components of Mg4FGeO6:Mn using the maximum entropy method.

A thermographic phosphor's decay time distribution over its temperature-sensitive range was retrieved with the Maximum Entropy Method (MEM). A decay time distribution consists of a range of decay times, each with an associated weighting for the decay time component's prevalence in the analyzed decay curve. With the MEM, significant decay time contributions of a decay curve have high weighting and are therefore found as peaks in the decay time distribution, where the width and peak value are correlated with the relative weight of the decay time components. These peaks in the decay time distribution give increased insight into a phosphor's lifetime behavior, which often cannot accurately be represented by a single or even two decay time components. The changes in the location of peaks in the decay time distribution with temperature can be used for thermometry, and this method has the benefit of being less sensitive to the multi-exponentiality of phosphor decay than mono-exponential decay time fitting. The method also resolves the underlying decay components with no assumptions of the number of significant decay time components. Initially, when the decay time distribution of Mg4FGeO6:Mn was captured, the collected decay included decaying luminescence from the alumina oxide tube in the tube furnace. Therefore, a second calibration was performed where the luminescence from the alumina oxide tube was minimized. These two calibration datasets were used to demonstrate that the MEM could characterize decays from two separate sources simultaneously.

Predicting Groundwater Levels in Ogallala Aquifer Wells Using Hierarchical Cluster Analysis and Artificial Neural Networks

The Ogallala Aquifer, located in the Central Plains of the United States, is essential for agricultural irrigation and public water supply. Indiscriminate pumping from the aquifer has caused several negative impacts, such as deterioration of water quality and depletion of groundwater levels, which urgently demand better management. This paper applies hierarchical cluster analysis (HCA) and artificial neural networks (ANNs) for predicting annual groundwater levels in 403 wells of the Ogallala Aquifer. First, the methodology employed HCA to cluster homogeneous wells based on the time series of groundwater levels. Then, the study calibrated an ANN model for each cluster (composed of one or more wells) using previous annual values of groundwater levels as input. The HCA results showed a particular pattern in the spatial distribution of the 30 found clusters, revealing that the Ogallala Aquifer holds higher groundwater levels in the western part, which gradually decrease, advancing to the east. The ANN models provided proper predictions even for wells outside of the calibration data set. This investigation concludes that the integration of HCA and ANN enabled single models to accurately forecast annual groundwater levels for sets of wells in the Ogallala Aquifer.

Performance of APSIM to Simulate the Dynamics of Winter Wheat Growth, Phenology, and Nitrogen Uptake from Early Growth Stages to Maturity in Northern Europe

Performance of the APSIM (Agricultural Production Systems sIMulator) wheat model was assessed to simulate winter wheat phenology, biomass, grain yield, and nitrogen (N) uptake for its potential to optimize fertilizer applications for optimal crop growth and minimal environmental degradation. The calibration and evaluation dataset had 144 and 72 different field growing conditions (location (~7) × year (~5) × sowing date (2) × N treatment (7-13)), respectively, and included seven cultivars. APSIM simulated phenological stages satisfactorily with both model calibration and evaluation data sets with r2 of 0.97 and RMSE of 3.98-4.15 BBCH (BASF, Bayer, Ciba-Geigy, and Hoechst) scale. Simulations for biomass accumulation and N uptake during early growth stages (BBCH 28-49) were also reasonable with r2 of 0.65 and RMSE of 1510 kg ha-1, and r2 of 0.64-0.66 and RMSE of 28-39 kg N ha-1, respectively, with a higher accuracy during booting (BBCH 45-47). Overestimation of N uptake during stem elongation (BBCH 32-39) was attributed to (1) high inter-annual variability in simulations, and (2) high sensitivity of parameters regulating N uptake from soil. Calibration accuracy of grain yield and grain N was higher than that of biomass and N uptake at the early growth stages. APSIM wheat model showed high potential for optimizing fertilizer management in winter wheat cultivation in Northern Europe.

Integrating random forest and synthetic aperture radar improves the estimation and monitoring of woody cover in indigenous forests of South Africa

Woody canopy cover (CC) is important for characterising terrestrial ecosystems and understanding vegetation dynamics. The lack of accurate calibration and validation datasets for reliable modelling of CC in the indigenous forests in South Africa contributes to uncertainties in carbon stock estimates and limits our understanding of how they might influence long-term climate change. The aim of this study was to develop a method for monitoring CC in the Dukuduku indigenous forest in South Africa. Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics of 2008, 2015, and 2018, polarimetric features, and Grey Level Co-occurrence Matrix (GLCMs) were used. Machine learning models Random Forest (RF) vs Support Vector Machines (SVM) were developed and calibrated using Collect Earth Online (CEO) data, a free and open-access land monitoring tool developed by the Food and Agriculture Organisation (FAO). The addition of GLCMs produced the highest accuracy in 2008, R2 (RMSE) = 0.39 (36.04%), and in 2015, R2 (RMSE) = 0.51 (27.82%), and in 2018, only SAR variables gave the highest accuracy R2 (RMSE) = 0.55 (29.50). The best-performing models for 2008, 2015, and 2018 were based on RF. During the ten-year study period, shrubland and wooded grassland had the highest transition, at 6% and 13%, respectively. The observed changes in the different canopies provide valuable insights into the vegetation dynamics of the Dukuduku indigenous forest. The modelling results suggest that the CEO calibration data can be improved by integrating airborne LiDAR data.

Developing In Situ Chemometric Models with Raman Spectroscopy for Monitoring an API Disproportionation with a Complex Polymorphic Landscape.

An in situ Raman method was developed to characterize the disproportionation of two salts involving a complex polymorphic landscape comprising up to two metastable and one stable freebase forms. Few precedents exist for Raman calibration procedures for solid form quantitation involving more than two polymorphs, while no literature examples were found for cases with multiple metastable forms. Therefore, a new Raman calibration procedure was proposed by directly using disproportionation experiments to generate multiple calibration samples encompassing a range of polymorph ratios through in-line Raman measurements complemented by off-line reference X-ray diffraction measurements. The developed Raman methods were capable of accurately quantitating each solid form in situ when solid concentration variation was incorporated into the calibration dataset. The kinetic understanding of the thermodynamically driven polymorphic conversions gained from this Raman method guided the selection of the salt best suited for the delivery of the active ingredient in the drug product. This work provided a spectroscopic and mathematical approach for simultaneously quantitating multiple polymorphs from a complex mixture of solids with the objective of real-time monitoring.

Testing Thermostatic Bath End-Scale Stability for Calibration Performance with a Multiple-Sensor Ensemble Using ARIMA, Temporal Stochastics and a Quantum Walker Algorithm.

Thermostatic bath calibration performance is usually checked for uniformity and stability to serve a wide range of industrial applications. Particularly challenging is the assessment at the limiting specification ends where the sensor system may be less effective in achieving consistency. An ensemble of eight sensors is used to test temperature measurement stability at various topological locations in a thermostatic bath (antifreeze) fluid at -20 °C. Eight streaks of temperature data were collected, and the resulting time-series were processed for normality, stationarity, and independence and identical distribution by employing regular statistical inference methods. Moreover, they were evaluated for autoregressive patterns and other underlying trends using classical Auto-Regressive Integrated Moving Average (ARIMA) modeling. In contrast, a continuous-time quantum walker algorithm was implemented, using an available R-package, in order to test the behavior of the fitted coefficients on the probabilistic node transitions of the temperature time series dataset. Tracking the network sequence for persistence and hierarchical mode strength was the objective. The quantum walker approach favoring a network probabilistic framework was posited as a faster way to arrive at simultaneous instability quantifications for all the examined time-series. The quantum walker algorithm may furnish expedient modal information in comparison to the classical ARIMA modeling and in conjunction with several popular stochastic analyzers of time-series stationarity, normality, and data sequence independence of temperature end-of-scale calibration datasets, which are investigated for temporal consistency.

Modelling nitrogen management in hybrid rice for coastal ecosystem of West Bengal, India.

Hybrid rice requires adequate nitrogen (N) management in order to achieve good yields from its vegetative and reproductive development. With this backdrop, a field experiment was conducted at Regional Research Station (Coastal Saline Zone), Bidhan Chandra Krishi Viswavidyalaya, Kakdwip, West Bengal (India) to record growth and yield performance of hybrid rice (cv. PAN 2423) under varied N-fertilizer doses. A modelling approach was adopted for the first time in hybrid rice production system under coastal ecosystem of West Bengal (India). In the present study, the Agricultural Production Systems Simulator (APSIM) model was calibrated and validated for simulating a hybrid rice production system with different N rates. The APSIM based crop simulation model was found to capture the physiological changes of hybrid rice under varied N rates effectively. While studying the relationship between simulated and observed yield data, we observed that the equations developed by APSIM were significant with higher R2 values (≥0.812). However, APSIM caused an over-estimation for calibrate data but it was rectified for validated data. The RMSE of models for all the cases was less than respective SD values and the normalized RMSE values were ≤20%. Hence, it was proved to be a good rationalized modelling and the performance of APSIM was robust. On the contrary, APSIM underestimated the calibrated amount of N (kg ha-1) in storage organ of hybrid rice, which was later rectified in case of validated data. A strong correlation existed between the observed and APSIM-simulated amounts of N in storage organ of hybrid rice (R2 = 0.94** and 0.96** for the calibration and validation data sets, respectively), which indicates the robustness of the APSIM simulation study. Scenario analysis also suggests that the optimal N rate will increase from 160 to 200 kg N ha-1 for the greatest hybrid rice production in coming years under elevated CO2 levels in the atmosphere. The APSIM-Oryza crop model had successfully predicted the variation in aboveground biomass and grain yield of hybrid rice under different climatic conditions.

Evaluation of pedotransfer functions to estimate some of soil hydraulic characteristics in North Africa: A case study from Morocco

Soil hydraulic properties are an important factor to optimize and adapt water management for a given crop. Pedotransfer functions (PTFs) present a solution to predict soil variables such as hydraulic properties, using fundamental soil properties. In this research, we compared two sources of soil information: iSDAsoil data and field data, in four regions in Morocco. We then used this data to evaluate published data and developed new PTFs using soil information to estimate soil gravimetric moisture content at saturation (w0), field capacity (w330) and permanent wilting point (w15000). A total of 331 samples were collected from four regions: Doukkala, Gharb-Loukous, Moulouya and Tadla. The data was divided into calibration and validation datasets. For development of different PTFs, we used simple linear regression, multiple linear regression, regression tree, Cubist algorithm, and random forest approaches. PTFs developed by Dijkerman (Geoderma, 1988, 42, 29–49) presented the best performance, showing lower RMSE, Bias and MAE compared to other PTFs. Using multiple linear regression to develop PTFs, models based on clay, silt and soil organic matter as input variables showed the best performance after calibration (R2 of 0.590, 0.785, 0.786 for w0, w330, and w15000, respectively). Regarding the techniques based on machine learning, random forest showed the best performance after calibration compared with other algorithms (R2 of 0.930, 0.955, 0.954 for w0, w330, and w15000, respectively). PTFs represent a low cost and easy technique to estimate soil hydraulic properties, to improve water management efficiency for the farmers.

Large-scale calibration method for MEMS-based projector 3D reconstruction.

Projectors based on Micro-Electro-Mechanical System (MEMS) have the advantages of small size and low cost. Moreover, uniaxial MEMS projectors have high projection accuracy, and have been widely used in structured light 3D reconstruction. However, the existing calibration methods for uniaxial MEMS projectors are not effective in large-scale scenes. To solve this problem, this paper proposes a novel efficient large-scale calibration method, which is easily implemented. The proposed method first calibrates a partial light plane for a fixed sampling period, then obtains the rest of the light plane by exploiting a non-fixed rotating shaft linear interpolation method. Experimental results verify that the proposed method attains high accuracy in a large depth field with only 11 sets of calibration data. Specifically, at a distance of 3000mm, the standard deviation of the plane fitting error reaches 0.2584mm on the standard plane, and the measurement accuracy attains 0.9124mm on the standard step object with 200mm interval.