Model Calibration Research Articles

Evaluation of a Multivariate Calibration Model for the WET Sensor That Incorporates Apparent Dielectric Permittivity and Bulk Soil Electrical Conductivity

The measurement of apparent dielectric permittivity (εs) by low-frequency capacitance sensors and its conversion to the volumetric water content of soil (θ) through a factory calibration is a valuable tool in precision irrigation. Under certain soil conditions, however, εs readings are substantially affected by the bulk soil electrical conductivity (ECb) variability, which is omitted in default calibration, leading to inaccurate θ estimations. This poses a challenge to the reliability of the capacitance sensors that require soil-specific calibrations, considering the ECb impact to ensure the accuracy in θ measurements. In this work, a multivariate calibration equation (multivariate) incorporating both εs and ECb for the determination of θ by the capacitance WET sensor (Delta-T Devices Ltd., Cambridge, UK) is examined. The experiments were conducted in the laboratory using the WET sensor, which measured θ, εs, and ECb simultaneously over a range of soil types with a predetermined actual volumetric water content value (θm) ranging from θ = 0 to saturation, which were obtained by wetting the soils with four water solutions of different electrical conductivities (ECi). The multivariate model’s performance was evaluated against the univariate CAL and the manufacturer’s (Manuf) calibration methods with the Root Mean Square Error (RMSE). According to the results, the multivariate model provided the most accurate θ estimations, (RMSE ≤ 0.022 m3m−3) compared to CAL (RMSE ≤ 0.027 m3m−3) and Manuf (RMSE ≤ 0.042 m3m−3), across all the examined soils. This study validates the effects of ECb on θ for the WET and recommends the multivariate approach for improving the capacitance sensors’ accuracy in soil moisture measurements.

192 Prediction of fecal starch content of fattening cattle using near-infrared spectroscopy and machine learning

Abstract Starch content in cattle feces can be used to predict starch digestibility. Near-infrared spectroscopy (NIRS) can be used to rapidly and non-chemically predict fecal starch content after a calibration model has been consequentially developed. Recent advancements in machine learning have introduced potent methods for developing optimized models. In addition, diverse preprocessing methods, such as scatter correction and smoothing, are available to eliminate irrelevant variance sources. This study aimed to identify optimal spectral preprocessing and regression methods for predicting fecal starch content using NIR spectra. NIR spectra were measured for 196 fecal samples collected from 9 fattening cattle farms. Fecal starch content on a DM basis was measured using enzymatic reactions and colorimetry. The 196 samples were divided into a calibration set (n = 123), a validation set (n = 29) comprising four farms, and an external validation set (n = 44) comprising five farms that differed from the calibration set. Based on the calibration set, two preprocessing methods were selected using the partial least squares (PLS) model. In method 1, the calibration models were developed by a grid search 5-fold cross validation on the 1,152 preprocessed calibration sets, and preprocessing with the lowest root mean square error (RMSE) of cross-validation among these calibration sets was selected. In method 2, the original calibration set (n = 123) was divided into a calibration set (n = 116) and a preprocessing selection set (n = 7) with different neutral detergent fiber and similar starch contents. After preprocessing, calibration models were developed as described in method 1. The preprocessing selection sets were then predicted by the developed models, and preprocessing with the lowest RMSE among these preprocessing selection sets was selected. Using the preprocessing methods selected in methods 1 and 2, the validation and external validation sets were predicted using seven regression models. As a result, the preprocessing selected by method 1 was in the wavelength range of 400–2500 nm, with robust normal variate (RNV) scatter correction and first-order derivative Savitzky-Golay filtering (2nd order polynomial, 2-nm window size). The preprocessing selected by method 2 had a wavelength range of 1500–2500 nm, RNV scatter correction, and 2nd-order derivative Savitzky-Golay filtering (3rd order polynomial, 25-nm window size). Among the seven regression models, the Lasso model was the most accurate for prediction. The Lasso models based on the preprocessing methods selected by methods 1 and 2 had RMSE of external validation = 2.054% of DM and 1.540% of DM, respectively. The feature importance of the Lasso model based on preprocessing method selected by methods 2 was higher at wavelengths of 1775 nm and 2325 nm. In conclusion, seven models were developed and evaluated based on diverse preprocessing methods using NIRS. The Lasso model exhibited the highest accuracy.

Spatially fractionated GRID radiation potentiates immune-mediated tumor control

BackgroundTumor-immune interactions shape a developing tumor and its tumor immune microenvironment (TIME) resulting in either well-infiltrated, immunologically inflamed tumor beds, or immune deserts with low levels of infiltration. The pre-treatment immune make-up of the TIME is associated with treatment outcome; immunologically inflamed tumors generally exhibit better responses to radio- and immunotherapy than non-inflamed tumors. However, radiotherapy is known to induce opposing immunological consequences, resulting in both immunostimulatory and inhibitory responses. In fact, it is thought that the radiation-induced tumoricidal immune response is curtailed by subsequent applications of radiation. It is thus conceivable that spatially fractionated radiotherapy (SFRT), administered through GRID blocks (SFRT-GRID) or lattice radiotherapy to create areas of low or high dose exposure, may create protective reservoirs of the tumor immune microenvironment, thereby preserving anti-tumor immune responses that are pivotal for radiation success.MethodsWe have developed an agent-based model (ABM) of tumor-immune interactions to investigate the immunological consequences and clinical outcomes after 2Gy×35\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2\\,\ ext{Gy} \ imes 35$$\\end{document} whole tumor radiation therapy (WTRT) and SFRT-GRID. The ABM is conceptually calibrated such that untreated tumors escape immune surveillance and grow to clinical detection. Individual ABM simulations are initialized from four distinct multiplex immunohistochemistry (mIHC) slides, and immune related parameter rates are generated using Latin Hypercube Sampling.ResultsIn silico simulations suggest that radiation-induced cancer cell death alone is insufficient to clear a tumor with WTRT. However, explicit consideration of radiation-induced anti-tumor immunity synergizes with radiation cytotoxicity to eradicate tumors. Similarly, SFRT-GRID is successful with radiation-induced anti-tumor immunity, and, for some pre-treatment TIME compositions and modeling parameters, SFRT-GRID might be superior to WTRT in providing tumor control.ConclusionThis study demonstrates the pivotal role of the radiation-induced anti-tumor immunity. Prolonged fractionated treatment schedules may counteract early immune recruitment, which may be protected by SFRT-facilitated immune reservoirs. Different biological responses and treatment outcomes are observed based on pre-treatment TIME composition and model parameters. A rigorous analysis and model calibration for different tumor types and immune infiltration states is required before any conclusions can be drawn for clinical translation.

Machine Learning Models to Predict Early Breakthrough of Recalcitrant Organic Micropollutants in Granular Activated Carbon Adsorbers.

Granular activated carbon (GAC) adsorption is frequently used to remove recalcitrant organic micropollutants (MPs) from water. The overarching aim of this research was to develop machine learning (ML) models to predict GAC performance from adsorbent, adsorbate, and background water matrix properties. For model calibration, MP breakthrough curves were compiled and analyzed to determine the bed volumes of water that can be treated until MP breakthrough reaches ten percent of the influent MP concentration (BV10). Over 400 data points were split into training, validation, and testing sets. Seventeen variables describing MP, background water matrix, and GAC properties were explored in ML models to predict log10-transformed BV10 values. Using the ML models on the testing set, predicted BV10 values exhibited mean absolute errors of ∼0.12 log units and were highly correlated with experimentally determined values (R2 ≥ 0.88). The top three drivers influencing BV10 predictions were the air-hexadecane partition coefficient and hydrogen bond acidity (Abraham parameters L and A) of the MPs and the dissolved organic carbon concentration of the GAC influent water. The model can be used to rapidly estimate the GAC bed life, select effective GAC products for a given treatment scenario, and explore the suitability of GAC treatment for remediating emerging MPs.

Multivariate calibration in smartphones using digital images for the determination of pH of drinking water

The execution of calibration models was previously restricted to computers, but with technological advances, applications on mobile devices have made this process more accessible. Photometrix PRO stands out as an innovative tool for creating calibration models from digital images. The colorimetric chart, widely used to determine the pH of solutions by visual analysis, can present subjectivity in interpretation. This study aims to develop and evaluate calibration models (PLS) using Photometrix PRO to accurately correlate the visual information of a colorimetric pH chart from the company AlfaKit. The application was used to acquire images, generate calibration curves and obtain pH values from prediction and validation sets. The curves presented R2, RMSEC and RMSECV that indicated good fit of the models. The t-test, with 95% confidence, showed that there was no significant difference between the results of the models and the colorimetric method. The Residual Prediction Deviation (RPD) ranged from 1.12 (RGB) to 5.37 (RGBHSVLI), making the model (RGBHSVLI) suitable for quality control. The study successfully demonstrated the feasibility and effectiveness of smartphone-based multivariate calibration (PLS) for determining pH in drinking water.

Building better forecasting pipelines: A generalizable guide to multi-output spatio-temporal forecasting

The demand for accurate Multi-Output Spatio-temporal Forecasting is rising in areas like public safety, urban mobility, and climate variability. Traditional methods struggle with model calibration and data integration. This paper presents a methodological guideline for creating forecasting pipelines that handle multi-output forecasting complexities. Using a uniform methodology tested on three diverse datasets, the framework combines genetic algorithms and advanced models to optimize forecasting. Our evaluation shows significant performance improvements, with better adaptability to urban and rural datasets, aiding decision-making in spatio-temporal analysis. The framework achieved a 20% average improvement in the R2 metric across all datasets, outperforming benchmark models.

Developing a socio-hydrological model to assess community sensitivity to lake degradation

ABSTRACT The crucial challenge in arid/developed endorheic basins is to balance agricultural water demands with ecosystem health. This study addresses this dilemma by developing a semi-distributed socio-hydrological model for the basins using monthly water balance coupled with annual community sensitivity (CS)/response models. The central research question focuses on whether the degradation of lakes in the Tashk-Bakhtegan basin, our selected case study, can shift the community’s attitude towards allocating water resources for environmental rather than agricultural purposes. Given the basin’s vastness and complex water access, individuals hold diverse environmental attitudes on lake degradation. Upon model calibration, it becomes evident that downstream areas exhibit a CS variable 2.4 times higher than upstream areas, with annual fluctuations ranging from 1.15 to 6.34. Remarkably, the findings underscore a gradual transformation in downstream attitudes, where increasing CS has shifted their perspective towards prioritizing the well-being of the lake, while upstream residents continue to prioritize agro-economic benefits.

Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers

This paper addresses the computational challenges inherent in the stochastic characterization and uncertainty quantification of Micro-Electro-Mechanical Systems (MEMS) capacitive accelerometers. Traditional methods, such as Markov Chain Monte Carlo (MCMC) algorithms, are often constrained by the computational intensity required for high-fidelity (e.g., finite element) simulations. To overcome these limitations, we propose to use supervised learning-based surrogate models, specifically artificial neural networks, to effectively approximate the response of MEMS capacitive accelerometers. Our approach involves training the surrogate models with data derived from initial high-fidelity finite element analyses (FEA), providing rich datasets to be generated in an offline phase. The surrogate models replicate the FEA accuracy in predicting the behavior of the accelerometer under a wide range of fabrication parameters, thereby reducing the online computational cost without compromising accuracy. This enables extensive and efficient stochastic analyses of complex MEMS devices, offering a flexible framework for their characterization. A key application of our framework is demonstrated in estimating the sensitivity of an accelerometer, accounting for unknown mechanical offsets, over-etching, and thickness variations. We employ an MCMC approach to estimate the posterior distribution of the device’s unknown fabrication parameters, informed by its response to transient voltage signals. The integration of surrogate models for mapping fabrication parameters to device responses, and subsequently to sensitivity measures, greatly enhances both backward and forward uncertainty quantification, yielding accurate results while significantly improving the efficiency and effectiveness of the characterization process. This process allows for the reconstruction of device sensitivity using only voltage signals, without the need for direct mechanical acceleration stimuli.

Remote sensing and big data: Google Earth Engine data to assist calibration processes in hydro-sediment modeling on large scales

Mathematical modeling aids in understanding large-scale erosion and sedimentation. However, sediment transport models calibration is constrained by data scarcity. This study explores the use of remote sensing (RS) imagery to supplement observed data, addressing three key questions: (1) How can high-resolution RS data be obtained using cloud-based methods for hydro-sediment applications, considering river changes? (2) What are the benefits of RS data in data-scarce conditions? (3) How can RS data improve hydro-sediment modeling in data-deficient regions? We developed a method to acquire large-scale RS data using Google Earth Engine (GEE) to obtain red and infrared reflectance from satellite imagery. After filtering errors, the data were used to calibrate a hydro-sediment model. Results showed that RS data, when combined with observed data, provided similar outcomes but performed better for lower values. Calibration with RS data alone improved the Kling-Gupta Efficiency (KGE) by 5%–18% and correlation by 5%–15%. Key conclusions are: (I) Cloud-based calibration is superior to using limited virtual stations; (II) RS data effectively complements observed data in hydro-sediment modeling; (III) Calibration using only RS data is beneficial in ungauged basins and preferable to no calibration.

Short wave-near infrared spectroscopy for predicting soluble solid content in intact mango with variable selection algorithms and chemometric model

Mango sweetness is one of the most prominent internal quality constituents that attract consumer's attention. The current common methods used to determine the sweetness of mango have significant disadvantages in that labor-intensive, time-consuming, and damaging techniques. This research aims to predict sweetness in mangoes using short wave-near infrared (SW-NIR) spectroscopy in the ranges 900–1650 nm, along with variable selection algorithms and chemometric model. A total of 120 mango samples were used to collect the spectra using a fibre module SW-NIR spectrometer. The partial least squares (PLS) regression with several spectral preprocessing methods was employed to develop the calibration model, and the best preprocessing technique was selected. The Savitzky–Golay second-derivative preprocessing technique performed better among the other preprocessing techniques with a correlation coefficient of prediction (rpred) of 0.74 and standard error of prediction (SEP) is 0.78 %Brix. After that, two variable selection techniques were used to select effective wavelength variables, including regression coefficient and successive projections algorithm (SPA). For SSC prediction in the range 900–1650 nm, the SPA-PLS model obtained a rpred of 0.78 and SEP of 0.67 %Brix. The current study unequivocally shows that the proposed SW-NIR spectroscopy coupled with a suitable chemometrics method can evaluate mango sweetness nondestructively.

Unravelling red deer (Cervus elaphus) meat adulteration in gourmet foods by quantitative real-time PCR

Red deer (Cervus elaphus) meat is highly regarded as an exclusive gourmet food due to its sensory quality, nutritional value and premium price, thus being susceptible to adulteration practices by its substitution with inferior meat species. This study proposes a novel real-time PCR method targeting the troponin I gene for detecting and quantifying red deer meat in processed food products. The assay showed acceptable performance parameters, achieving absolute and relative limits of quantification of 0.01 ng of DNA and 0.5% (w/w) of red deer meat in both raw and thermally processed pâté. The assay parameters, using raw and autoclaved reference mixtures, regarding PCR efficiency (89.3% and 100.0%, respectively), slope of the calibration curve (−3.6067 and −3.3224, respectively) and R2 (0.9889 and 0.9893, respectively) highlight the high performance of the calibration models for the determination of red deer meat. The method was successfully validated using blind mixtures and applied to 46 commercial meat products from Poland, Portugal and Spain. Data suggested the adulteration of 48% of the samples by the total or partial substitution of red deer species and the suitability of the method for the routine testing of game meat products in quality control laboratories.

A Passive Perspiration Inspired Wearable Platform for Continuous Glucose Monitoring.

The demand for glucose monitoring devices has witnessed continuous growth from the rising diabetic population. The traditional approach of blood glucose (BG) sensor strip testing generates only intermittent glucose readings. Interstitial fluid-based devices measure glucose dynamically, but their sensing approaches remain either minimally invasive or prone to skin irritation. Here, a sweat glucose monitoring system is presented, which completely operates under rest with no sweat stimulation and can generate real-time BG dynamics. Osmotically driven hydrogels, capillary action with paper microfluidics, and self-powered enzymatic biochemical sensor are used for simultaneous sweat extraction, transport, and glucose monitoring, respectively. The osmotic forces facilitate greater flux inflow and minimize sweat rate fluctuations compared to natural perspiration-based sampling. The epidermal platform is tested on fingertip and forearm under varying physiological conditions. Personalized calibration models are developed and validated to obtain real-time BG information from sweat. The estimated BG concentration showed a good correlation with measured BG concentration, with all values lying in the A+B region of consensus error grid (MARD = 10.56% (fingertip) and 13.17% (forearm)). Overall, the successful execution of such osmotically driven continuous BG monitoring system from passive sweat can be a useful addition to the next-generation continuous sweat glucose monitors.

Fractionated photoimmunotherapy stimulates an anti-tumour immune response: an integrated mathematical and in vitro study

BackgroundAdvanced epithelial ovarian cancer (EOC) has high recurrence rates due to disseminated initial disease presentation. Cytotoxic phototherapies, such as photodynamic therapy (PDT) and photoimmunotherapy (PIT, cell-targeted PDT), have the potential to treat disseminated malignancies due to safe intraperitoneal delivery.MethodsWe use in vitro measurements of EOC tumour cell and T cell responses to chemotherapy, PDT, and epidermal growth factor receptor targeted PIT as inputs to a mathematical model of non-linear tumour and immune effector cell interaction. The model outputs were used to calculate how photoimmunotherapy could be utilised for tumour control.ResultsIn vitro measurements of PIT dose responses revealed that although low light doses (<10 J/cm2) lead to limited tumour cell killing they also increased proliferation of anti-tumour immune effector cells. Model simulations demonstrated that breaking up a larger light dose into multiple lower dose fractions (vis-à-vis fractionated radiotherapy) could be utilised to effect tumour control via stimulation of an anti-tumour immune response.ConclusionsThere is promise for applying fractionated PIT in the setting of EOC. However, recommending specific fractionated PIT dosimetry and timing will require appropriate model calibration on tumour-immune interaction data in human patients and subsequent validation of model predictions in prospective clinical trials.

Enhancing fruit SSC detection accuracy via a light attenuation theory-based correction method to mitigate measurement orientation variability

Nondestructive online detection and sorting for fruit quality has gradually attracted attention in the global agro-product industry. However, the detection accuracy is influenced by many factors, such as fruit orientation, fruit shape, and environmental fluctuations. This study aimed to explore the impact of measurement orientation variation on spectra and soluble solids content (SSC) detection in apples and propose a correction method to mitigate the effect. Firstly, the visible/near-infrared (Vis/NIR) spectra ranging from 550 to 950 nm were collected in four orientations. Then, calibration models were developed for each orientation separately (local models) and all orientations corporately (global models) to evaluate and compensate for the effect of orientation. After that, the novel method based on the light attenuation theory was introduced to correct the acquired raw spectra and establish corrected orientation models. Results showed that measurement orientation significantly altered spectral intensity due to variations in surface curvature and optical path, thus declining models’ predictive power and robustness. Global models proved to be less susceptible to orientation variation compared with local models. The performance of both local and global models considerably improved post-orientation correction, attributed to the decrease of spectral distribution difference, with their average Rp2 and RPD increased by 93.38 % and 8.11 %, 10.56 % and 10.57 %, respectively, while the average RMSEP decreased by 16.01 % and 10.78 %, respectively. Overall, this work provides a more cost-effective and universal approach to impair the influence of measurement orientation and improve the accuracy and reliability of fruit quality online detection.

Georeferenced X (formerly twitter) data as a proxy of mobility behaviour: case study of Norway

Georeferenced messages on social media represent a powerful data source to gain a different perspective for estimating mobility behaviour, which is still mainly based on travel surveys. These data are openly available, yet few studies have explored their potential. This paper assesses the feasibility of large-scale Twitter data as a proxy of human mobility behaviour to complement traditional travel surveys, and for calibration and validation of transport models. Almost 12 million Tweets from more than 90,000 users were further analysed to detect the trip patterns at municipality level in Norway from 2012 to 2022. Results showed that the mobility patterns changed between 2014 and 2019 for the travel survey, as for 2019 most of the reported trips were short and concentrated in the densely populated areas of the country, where most respondents lived, triggering a lack of information for certain areas. In contrast, Twitter data presented a more stable data source along both years with similar population distribution and average trip length. Although Twitter data have limitations in relation to the socio-demographic information of the users, it could complement the travel survey given the broader spatial and temporal distribution of this large-scale data.

Evaluation of hydrological models for streamflow prediction: a case study of the Mille River, Lower Awash Basin, Ethiopia

ABSTRACT Accurate prediction of stream flow is essential for effective water resource management, particularly in regions with complex hydrological dynamics. The Mille River, which flows through this basin and has a watershed area of 4,862.3 km², plays a crucial role in the region's water resources. However, selecting the most suitable hydrological model for this task remains a challenge due to varying model performances. This study addresses the problem of identifying the most effective hydrological model for predicting stream flow in the Mille River by evaluating three models: Soil and Water Assessment Tool (SWAT), Hydrologiska Byråns Vattenbalansavdelning (HBV-IHMS), and Artificial Neural Network (ANN). The objectives are to compare these models’ abilities to simulate daily stream flow and to determine their accuracy. The significance of this evaluation lies to guide water management decisions by identifying which model provides the most reliable predictions. The methodology involves using performance metrics, Nash-Sutcliffe Efficiency (NSE) values, to assess the accuracy of each model. The results show that SWAT achieved the highest NSE values of 0.81 and 0.82, indicating the best performance in simulating stream flow. ANN followed with NSE values of 0.79 and 0.81, while HBV-IHMS had lower NSE values of 0.73 and 0.74. Although all models demonstrated potential, SWAT and ANN outperformed HBV-IHMS in capturing the complex hydrological processes of the Mille River. The findings from this study indicate that the choice of model significantly impacts the accuracy and reliability of streamflow predictions. The SWAT and ANN models show strong potential for use in operational streamflow forecasting in the Mille River and similar basins. In conclusion, further research is recommended to refine model calibration and validation processes. Incorporating additional data sources, such as remote sensing and climate projections, could further enhance model accuracy and provide a more comprehensive understanding of the region's hydrological dynamics.

Improving sub-seasonal extreme precipitation forecasts over China through a hybrid statistical-dynamical framework

Skillful and reliable sub-seasonal extreme precipitation forecasts are crucial for disaster prevention and mitigation. In this study, we introduce a hybrid statistical-dynamical framework to predict monthly maximum one-day precipitation (Rx1D) and monthly maximum five-day precipitation (Rx5D) over China from May to October. In the hybrid statistical-dynamical framework, the ECMWF forecasts of precipitation and boreal summer intraseasonal oscillation (BSISO) indices are used as predictors to establish calibration model and bridging models, separately. The calibration model and bridging models are then merged to generate probabilistic forecasts of Rx1D and Rx5D. Our results suggest that the bridging models show better performance in predicting Rx1D and Rx5D than calibration model in May, June, and July when the BSISO indices are used as predictors. The forecast skill of calibration model is higher compared to bridging models in August, September, and October. The BMA merged forecasts take advantage of both calibration model and bridging models, and can provide skilful and reliable forecasts for both Rx1D and Rx5D prediction. To have a more comprehensive assessment, we also evaluate the prediction skill of the occurrence of extreme precipitation events with exceedance probabilities of 50%, 20%, and 5% for both Rx1D and Rx5D. The Brier skill score of merged forecasts indicates that the hybrid statistical-dynamical framework can also provide skilful forecasts for the occurrence of extreme precipitation events greater than one-in-5-year return value of Rx1D (5Rx1D) and one-in-5-year return value of Rx5D (5Rx5D) in comparison to long-term climatology. These findings demonstrate the great potential of combining dynamical models and statistical models in improving sub-seasonal extreme precipitation forecasts.

On-Demand Meal Delivery: A Markov Model for Circulating Couriers

On-demand meal delivery has become a feature of most cities around the world as a result of platforms and apps that facilitate it as well as the pandemic, which for a period, closed restaurants. Meals are delivered by couriers, typically on bikes, e-bikes, or scooters, who circulate collecting meals from kitchens and delivering them to customers, who usually order online. A Markov model for circulating couriers with n + 1 parameters, where [Formula: see text] is the number of kitchens plus customers, is derived by entropy maximization. There is one parameter for each kitchen and customer representing the demand for a courier, and there is one parameter representing the urgency of delivery. It is shown how the mean and variance of delivery time can be calculated once the parameters are known. The Markov model is irreducible. Two procedures are presented for calibrating model parameters on a data set of orders. Both procedures match known order frequencies with fitted visit probabilities; the first inputs an urgency parameter value and outputs mean delivery time, whereas the second inputs mean delivery time and outputs the corresponding urgency parameter value. Model calibration is demonstrated on a publicly available data set of meal orders from Grubhub. Grubhub data are also used to validate the calibrated model using a likelihood ratio. By changing the location of one kitchen, it is shown how the calibrated model can estimate the resulting change in demand for its meals and the corresponding mean delivery time. The Markov model could also be used for the assignment of courier trips to a street network. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT Conference.

Calibration and validation of the AquaCrop model for forage cactus production systems under different management interventions in the semi-arid region of Brazil

Simulation models are useful tools for estimating plant response and contributing to the development of management strategies and yield predictions in crops. This study aimed to calibrate and evaluate the AquaCrop-FAO model for the forage cactus imposed on different cropping systems in the semi-arid region of Brazil. Four experiments were conducted: cactus-sorghum intercropping system with five spacings between plants (0.10, 0.20, 0.30, 0.40 and 0.50 m); cactus-sorghum intercropping system with five spacings between rows (1.00, 1.25, 1.50 and 1.75 m); single cactus with four levels of mulch (0, 5, 10 and 15 Mg ha−1); and single forage cactus with four levels of nitrogen fertilisation (50, 150, 300 and 450 kg of N ha−1). The performance of the model was evaluated using the following parameters: root mean squared error (RMSE); normalised RMSE (NRMSE), coefficients of determination (R2), Nash-Sutcliffe model efficiency coefficient (ME) and the Willmott index of agreement (d). During the calibration stage, the statistical indices pointed to the good performance of the AquaCrop model in estimating dry biomass in most of the cropping systems, with 10 < NRMSE <20; R2 > 0.96 and ME > 0.95. The AquaCrop model showed the best performance simulating systems under different levels of nitrogen fertilisation. Among the intercropping systems, the denser arrangements showed the highest values for simulated biomass.

Free fatty acids and mortality among adults in the United States: a report from US National Health and Nutrition Examination Survey (NHANES)

BackgroundThe relationship between free fatty acids (FFAs) and the risk of mortality remains unclear. There is a scarcity of prospective studies examining the associations between specific FFAs, rather than total concentrations, of their effect on long-term health outcomes.ObjectiveTo evaluate the correlation between different FFAs and all-cause and cardiovascular mortality in a large, diverse, nationally representative sample of adults in the US, and examine how different FFAs may mediate this association.MethodsThis cohort study included unsaturated fatty acids (USFA) and saturated fatty acids (SFA) groups in the US National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014 and provided blood samples for FFAs levels. Multiple model calibration was performed using Cox regression analysis for known risk factors to explore the associations between FFAs and all-cause and cardiovascular mortality.ResultsIn the group of USFA, 3719 people were included, median follow-up, 6.7 years (5.8–7.8 years). In the SFA group, we included 3900 people with a median follow-up, 6.9 years (5.9-8 years). In the USFA group, myristoleic acid (14:1 n-5) (hazard ratio (HR) 1.02 [1.006–1.034]; P = 0.004), palmitoleic acid (16:1 n-7) (HR 1.001 [1.001–1.002]; P < 0.001), cis-vaccenic acid (18:1 n-7) (HR 1.006 [1.003–1.009]; P < 0.001), nervonic acid (24:1 n-9) (HR 1.007 [1.002–1.012]; P = 0.003), eicosatrienoic acid (20:3 n-9) (HR 1.027 [1.009–1.046]; P = 0.003), docosatetraenoic acid (22:4 n-6) (HR 1.024 [1.012–1.036]; P < 0.001), and docosapentaenoic acid (22:5 n-6) (HR 1.019 [1.006–1.032]; P = 0.005) were positively associated with the all-cause mortality, while docosahexaenoic acid (22:6 n-3) had a statistically lower risk of all-cause mortality (HR 0.998 [0.996–0.999]; P = 0.007). Among the SFA group, palmitic acid (16:0) demonstrated a higher risk of all-cause mortality (HR 1.00 [1.00–1.00]; P = 0.022), while tricosanoic acid (23:0) (HR 0.975 [0.959–0.991]; P = 0.002) and lignoceric acid (24:0) (HR 0.992 [0.984–0.999]; P = 0.036) were linked to a lower risk of all-cause mortality. Besides 23:0 and 24:0, the other FFAs mentioned above were linearly associated with the risks of all-cause mortality.ConclusionsIn this nationally representative cohort of US adults, some different FFAs exhibited significant associations with risk of all-cause mortality. Achieving optimal concentrations of specific FFAs may lower this risk of all-cause mortality, but this benefit was not observed in regards to cardiovascular mortality.