Abstract Affordable autonomous soil sensors and IoT technology enable real‐time soil moisture monitoring, which offers opportunities for real‐time model calibration and irrigation optimization. We introduce an irrigation decision support system SWIM 2 (Sensor Wielded Inverse Modeling of a Soil Water Irrigation Model), a digital twin that integrates continuous sensor data and unbiased, periodic soil samples with an FAO‐based soil water balance model using a Bayesian inverse modeling algorithm, DREAM (ZS) (DiffeRential Evolution Adaptive Metropolis). SWIM 2 estimates 12 soil and crop parameters and their associated probability distributions and correlations, providing soil moisture predictions with uncertainty estimates. The SWIM 2 framework is illustrated and validated in a real‐time setup for 18 vegetable cropping cycles on agricultural fields in Flanders, Belgium, with in situ precipitation data. Although using minimal prior knowledge and despite sensor bias, SWIM 2 achieves robust soil moisture predictions for a 7‐day horizon, with accuracies comparable to sensor measurements. Predictions improve substantially in precision within the first 20 calibration days and maintain high predictive power throughout the growing season. The impact of in situ measurements and temporal covariance of the observational errors (“error covariance”) was assessed, indicating that good knowledge of the error covariance and independent soil moisture samples are essential to correct for sensor bias and ensure accurate model calibration, while continuous sensor data ensure accurate and precise estimates of the dynamics. This study demonstrates the use of soil moisture sensor data in a Bayesian inverse modeling framework, offering practical solutions for real‐time soil moisture prediction and irrigation decision‐making, enhancing water management across agricultural fields.

This paper discusses the mathematical model and simulation of Solid Oxide Fuel Cell (SOFC), where the conventional and reversible SOFC are studied. Their performance is studied at the steady state to get the V-I polarization curves, as well as in case of changing the electric current drawn from the cell to get the change in voltage over time. Unlike most existing studies, the focus is on the time needed by the cell to reach voltage stability after a change, in preparation for studying the cell integration into an electrical network, and all of these studies are carried out when changing the cell operating conditions such as temperatures and the flow rate of reactive gases. Then optimization techniques are used, such as Walrus Optimization Algorithm (WaOA), Secretary Bird Optimization Algorithm (SBOA), Chaos Game Optimization (CGO) and Teaching-Learning Based Optimization (TLBO) to increase model accuracy and make its results closer to the published laboratory results. The results demonstrate that WaOA achieves the lowest modeling error among the investigated optimization techniques and provides the most accurate model calibration. The proposed WaOA-based tuning reduces the modeling error by more than five orders of magnitude compared to curve-fitting approaches reported in the literature, and is further employed to optimize reactant flow rates, resulting in an output power increase of approximately 6% compared to nominal operating conditions.

In the aerospace industry, modern aircraft are increasingly equipped with a growing number of sensors, which enable the development of predictive maintenance solutions utilizing data-driven diagnostic and prognostic (D&P) techniques to enhance operational availability and reduce maintenance costs. However, constructing a purely data-driven D&P solution requires a substantial amount of run-to-fail sensor data, which is often unavailable for highly reliable and safety-critical aircraft systems. This limitation restricts the applicability of purely data-driven D&P solutions for aircraft subsystems. To address this limitation, we developed a novel Hybrid Digital Twin framework that integrates physics-based subsystem models with sensor data, enabling enhanced feature generation for improved fault diagnostics and prognostics. Our approach simultaneously estimates both design and health-related parameters, facilitating accurate model calibration even when some of design data is not available. Sensor features enhanced with estimated health-related parameters enable more accurate data-driven diagnostics and prognostics solutions of a sub-system or a component. The framework is demonstrated on key subsystems of the aircraft Environment Control System (ECS), including the Heat Exchanger and Centrifugal Compressor. Various parameter estimation techniques including nonlinear least squares, particle swarm optimization, and extended Kalman filter, Unscented Kalman filter, Physics-Informed Neural Networks, etc., are evaluated. This Hybrid Digital Twin approach offers a promising pathway for more accurate, robust and scalable health management of aircraft subsystems having limited operational data.

The accurate measurement of herbage mass is essential for feed budgeting and the management of sustainable and profitable grazing systems. There are many techniques available to estimate herbage mass in pastoral systems, and these vary in accuracy, cost, and time taken to implement. In situ and remote sensing techniques are both associated with moderate to high error, as herbage mass is affected by a number of dependent and independent factors, including sward composition, soil structure, chemical characteristics and moisture levels, climatic conditions, and grazing management, which must be considered in the development of an accurate local calibration model for precise estimation of herbage mass. This review provides an overview of commonly used herbage mass assessment techniques and describes their limitations, synergies, and trade-offs, and also covers the integration of new technologies which have the potential to monitor pastures at scale. This review highlights the need for further research and to integrate new technologies for accurate and precise measurement of herbage mass, noting the lack of calibration with in situ methods, the need for development of new protocols for assessment, variance in equipment and software compatibility, and the need to evaluate the effectiveness of methods/techniques on a variety of livestock operations for extended periods.

The slider-crank mechanism (SCM) is fundamental to various mechanical systems. However, optimizing its dynamic performance remains a pressing challenge due to excessive torque, joint reactions, and energy consumption. This study introduces two key innovations to address these challenges: (1) the integration of springs into SCM to optimize dynamic performance and (2) a novel hybrid optimization approach combining the Conjugate Direction with Orthogonal Shift (CDOS) method and Parameter Space Investigation (PSI). The mathematical model evaluates the effects of spring placement and stiffness on critical performance parameters such as energy efficiency, torque demands, and joint forces. The hybrid CDOS-PSI approach systematically identifies optimal design configurations to balance these performance objectives. The methodology’s efficacy is validated through a case study on a wood splitter, a commonly used agricultural and industrial machine. Experimental tests were carried out to measure splitting forces for different wood types, enabling accurate model calibration. Results demonstrate that the spring-integrated SCM reduces dynamic loads significantly compared to conventional designs. Comparative numerical analysis confirms the proposed model’s accuracy, with less than 5% deviations. This research offers innovative contributions to SCM design by combining spring-based dynamic enhancement with a novel hybrid optimization framework for improved efficiency and durability in practical applications.

Robot-assisted rehabilitation effectively enhances motor recovery in patients with mobility impairments. This study examines the REHAP system—a two-DOF mechatronic rehabilitation device for passive physiotherapy—focusing on dynamic modelling and energy-efficiency analysis. The Euler–Lagrange method was employed to derive the dynamic model, incorporating actuator parameters obtained through experimental characterisation of DC motors. We assessed energy consumption under various control strategies and mechanical-loading conditions. Results indicate that the choice of control strategy and the tuning of actuator parameters significantly impact system efficiency, highlighting the critical need for accurate model calibration. Integrating dynamic modelling improves both motion precision and energy economy, thereby enabling more sustainable rehabilitation technologies. This research underscores how energy-aware control strategies can enhance both performance and sustainability in robotic physiotherapy systems.

Lightning is an important yet often overlooked disturbance agent in forest ecosystems. Recent research conducted in Panama suggests that lightning is a major cause of large tree mortality in tropical forests. However, lightning-induced tree mortality is not included in state-of-the-art ecosystem models. Here, we implement a general lightning mortality module in the dynamic global vegetation model LPJ-GUESS to explore the impacts of lightning on forests at local and global scales. Lightning mortality was implemented stochastically in dependency of local cloud-to-ground lightning density and simulated forest structure based on findings from the Panamanian forest. For this site, LPJ-GUESS adequately simulates the average number of trees of different size classes killed per lightning strike, with a total of 2.9 simulated versus 3.2 observed. The model also captures the estimated contribution of lightning to the overall mortality of large trees (21% simulated vs. 24% observed). Applying the new model version to other tropical and temperate forests for which observation-based estimates on lightning mortality exist, LPJ-GUESS reproduces estimated impacts in some forests but simulates substantially lower impacts for others. Global simulations driven by two alternative products of cloud-to-ground lightning densities suggest that lightning kills 301-340 million trees annually, thereby causing 0.21-0.30 GtC yr.-1 of dead biomass (2.1%-2.9% of total killed biomass). The simulations also reveal that the global biomass would be 1.3%-1.7% higher in a world without lightning. Spatially, simulated lightning mortality is largest in the tropical forests of Africa. Although our simulations suggest an important role of lightning in forest ecosystems on a global scale, more data on lightning-induced tree mortality across different forest types would be desirable for more accurate model calibration and evaluation. Given the anticipated increase in future lightning activity, incorporating lightning mortality into ecosystem models is needed to obtain more reliable projections of terrestrial vegetation dynamics and carbon cycling.

ABSTRACT The process of selecting representative samples is crucial for establishing an accurate calibration model. To enhance the representativeness of the samples, a method for sample selection, utilizing the degree of anomaly as the evaluation criterion, is proposed. Initially, anomaly scores corresponding to various detection methods are obtained to ensure a comprehensive evaluation. These scores are then normalized by the confidence lower limit to establish a consistent scoring criterion. Subsequently, the weights of different detection methods are determined through eigenvector centrality analysis of a graph, where the methods serve as nodes and the similarity acts as weighted edges. Finally, the comprehensive anomaly scores are computed as the sum of weighted scores and are subsequently sorted. Representative samples are selected using a uniformly spaced sampling approach, with the spacing determined by a predefined and provided sample number. The efficacy of the method is validated across different sample sets.

Raman spectroscopy is utilised extensively in pharmaceutical analysis for tasks such as drug discovery, quality control and active pharmaceutical ingredient (API) development. Despite this, access to open-source Raman spectral datasets for modelling and analysis is often a challenge. In laboratory settings, small spectral libraries are typically compiled for one-shot identification of intermediates or unknown chemicals, which restricts availability to comprehensive and high-quality reference data. In this work, we introduce a new open-source Raman dataset consisting of pure chemical compounds commonly employed in the development of APIs. By curating and publishing this dataset, we aim to provide the scientific community with access to high-quality, reusable data. Containing 3,510 samples spanning 32 compounds, this data can be utilised for referencing and can potentially facilitate in the development of more accurate and generalisable calibration models when access to reference data is limited.

Soil is considered as the source of life on the globe. Although, numerous research studies, the soil is not completely understood whereas it is dynamic complex matrix include many simultaneous processes. The conventional methods of soil testing are the most reliable for assessing the land productivity and management. Unfortunately, these methods are time consuming and laborious as well as costly and hazardous to the environment. Therefore, the soil spectroscopy technique provides the functions of detecting, characterizing, quantifying and mapping several soil properties based on the uses of different kinds of the sensors (ground-based, airborne-based, and satellite-based). An integration of soil spectroscopic data, data processing, and modelling is considered as an effective tool for estimating soil parameters. Although these advanced techniques able to predict the majority of soil properties, there are some of these properties require more experiments for building accurate calibration models for estimation. Moreover, creating accurate soil spectral libraries (SSLs) for specific areas or soil types is very crucial for saving time, effort, expenses of soil surveying, sampling, and analysis. The application of these SSLs has a big limitation of the continuous variability of the soil properties over time. Thus, establishing extensive spectral libraries is important and mandatory for covering the small scaled areas’ variabilities as well as available soil types. Till now, the soil spectroscopy is not entirely replacing the conventional testing methods of soils because these techniques need future applications to be trusted and guaranteed of their effectiveness.

The rising heavy metal (HM) pollution in coastal aquifers in rapidly urbanizing areas such as Dammam leads to significant risks to public health and environmental sustainability, challenging compliance with Environmental Protection Agency (EPA) guidelines, World Health Organization (WHO) standards, and Sustainable Development Goals (SDGs) related to clean water and life on land. This study developed the predictive-based monitoring of HM concentrations, including cadmium (Cd), chromium (Cr), and mercury (Hg) in the coastal aquifers of Dammam, influenced by industrial, agricultural, and urban activities. For this purpose, dynamic system identification and machine learning (ML) models integrated with three ensemble techniques, namely, simple averaging (SAE), weighted averaging (WAE), and neuro-ensemble (N-ESB), were employed to enhance the accuracy, reliability, and efficiency of environmental monitoring systems. The experimental data were calibrated and validated in addition to k-fold cross-validation to ensure the predictive skills of the models. The methodology integrates extensive data collection across varied land uses in Dammam and accurate model calibration and validation phases to develop highly accurate predictive models. The findings proved that the N-ESB and Hammerstein-Wiener (HW) models surpassed other models in predicting the concentrations of all HM. For Cd, the N-ESB model achieved a root mean square error (RMSE = 0.0010mg/kg). Similarly, Cr demonstrated superior performance (RMSE = 0.0179mg/kg). Further numerical results indicated that the HW algorithm proved the most effective for Hg, with RMSE = 0.0000mg/kg. The quantitative comparison suggested that the N-ESB model's consistently high performance and low error rates make it an optimal choice for real-time, precise monitoring and management of HM pollution in coastal aquifers. The outcomes of this research highlighted the importance of integrating advanced predictive modeling techniques in environmental science, providing significant and practical implications for policymaking and ecological management.

Cottonseed is rich in oil and protein. However, its antinutritional factor content, of phytic acid (PA), has limited its utilization. Near-infrared (NIR) spectroscopy, combined with chemometrics, is an efficient and eco-friendly analytical technique for crop quality analysis. Despite its potential, there are currently no established NIR models for measuring the PA content in fuzzy cottonseeds. In this research, a total of 456 samples of fuzzy cottonseed were used as the experimental materials. Spectral pre-treatments, including first derivative (1D) and standard normal variable transformation (SNV), were applied, and the linear partial least squares (PLS), nonlinear support vector machine (SVM), and random forest (RF) methods were utilized to develop accurate calibration models for predicting the content of PA in fuzzy cottonseed. The results showed that the spectral pre-treatment significantly improved the prediction performance of the models, with the RF model exhibiting the best prediction performance. The RF model had a coefficient of determination in prediction (R2p) of 0.9114, and its residual predictive deviation (RPD) was 3.9828, which indicates its high accuracy in measuring the PA content in fuzzy cottonseed. Additionally, this method avoids the costly and time-consuming delinting and crushing of cottonseeds, making it an economical and environmentally friendly alternative.

Groundwater flow modelling is critical for managing groundwater resources, particularly amid climate change and rising water demand. This narrative review examines the role of groundwater flow models in sustainable water resource management, focusing on challenges and solutions to enhance model reliability. A key challenge is data limitation—especially in regions like sub-Saharan Africa and South Asia, where scarce hydrogeological data hinders accurate model calibration. The complexity of aquifer systems, such as karst aquifers in North America and fractured-rock aquifers in India, further complicates model development, requiring detailed geological data and complex simulations. Additionally, uncertainties arise from limited knowledge of aquifer properties, variable boundary conditions, and sparse monitoring networks, which can reduce model predictability. Despite these obstacles, groundwater flow models are essential for simulating groundwater behaviour in response to altered precipitation patterns, increasing extraction rates, and extreme events like droughts. For instance, predictive modelling has helped assess potential depletion risks in California’s Central Valley and contamination risks in industrial zones of East Asia, guiding sustainable extraction strategies and contamination assessments. To improve model reliability, this review emphasizes the need for enhanced data collection, integration of advanced technologies—such as artificial intelligence and machine learning for predictive accuracy—and the adoption of multidisciplinary modelling approaches. These advancements, improved sensor networks, and regional data-sharing initiatives are critical to reducing uncertainties and increasing model precision. Ultimately, such improvements will support climate adaptation efforts and promote the sustainable management of global groundwater resources, benefiting water managers and policy makers.

Traditional non-coplanar calibration methods such as Tsai’s method have many problems, such as insufficient calibration accuracy, inconvenient operation, inaccurate models, etc. This paper proposes a new high-precision non-coplanar calibration method that aims to solve these problems. Like Tsai’s method, the proposed calibration method utilizes a one-dimensional displacement stage and a two-dimensional plane target to generate a virtual 3D feature point sequence. As an improvement, an affine coordinate correction model is applied to ensure the accuracy and orthogonality of the obtained virtual 3D coordinates. A novel and accurate camera calibration model is further established. Compared with Tsai’s model, which uses a radial alignment constraint and ignores the orthonormal constraint of the rotation matrix, the proposed calibration model fully considers the degrees of freedom of the camera’s parameters to be calibrated, as well as the lens’s nonlinear distortion parameters. More accurate analytical solutions of intrinsic and extrinsic parameters can be obtained with the proposed calibration model. Finally, a novel high-precision non-coplanar calibration method is proposed based on the proposed calibration model. The reprojection experiment proves that the calibration accuracy of this calibration method is better than that of Tsai’s and Zhang’s calibration methods under the same calibration conditions. As a supplement, a novel binocular camera system extrinsic parameter calibration method with known intrinsic parameters is proposed. With accurate intrinsic and extrinsic parameters, the binocular camera system’s relative measurement accuracy could be within 1/10 000. Overall, this method can be used in experimental and industrial applications that require high-precision calibration parameters.

A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data

Laser ablation-inductively coupled plasma-time of flight mass spectrometry (LA-ICP-TOFMS) concentrations were used to develop accurate calibration models for laser-induced breakdown spectroscopy (LIBS) mappings of pegmatitic drill cores samples. Both methods were applied on the same area of drill core samples, providing two spatially-resolved datasets for this area. The datasets were aligned pixel by pixel to create a pixel-matched reference area that covered the heterogeneity of the complete drill core. This way, different matrix effects affecting LIBS intensities could be taken into account and accurate spatial quantification of Li2O, SiO2, Al2O3, Na2O, and K2O from LIBS measurements was enabled.In particular, LIBS intensities and LA-ICP-TOFMS concentrations of individual pixels of the reference area were used as the input for a linear Partial Least Square Regression (PLSR) and a non-linear Least Square Support Vector Machines (LS-SVM) calibration model. Varying numbers between 100 and 2000 pixels were used for model creation, and root mean square error (RMSE) and R2 of each model were compared. Better values were achieved for the LS-SVM calibration model. Based on these results, the PLSR model was discarded and only the LS-SVM model with 1000 train pixels was further validated.For two different validation areas, LA-ICP-TOFMS concentrations were compared to LIBS-based concentrations obtained from the LS-SVM calibration model. The spatially-resolved quantification results of the LIBS data agree very well with the independently analysed LA-ICP-TOFMS concentrations, which is e.g. reflected in R2 values between 0.83 and 0.96 (mean 0.89) for Li2O, SiO2, Al2O3, Na2O, and K2O. In general, it was shown that the combination of LA-ICP-TOFMS and LIBS yields a comprehensive dataset for robust multivariate calibration. This enables spatially-resolved LIBS-based quantification of pegmatite samples that are subject to significant physical and chemical matrix effects.

The paper shows the use of the methods of hyperspectral imaging (HSI) in a range of 400–1000 nm and multivariate analysis for sorting Hass avocado fruits. The decomposition of the data matrix of HSIs of avocado fruits was carried out using the principle component analysis. The reflection bands in the visible and near-infrared spectral regions interrelated with the process of maturation and the moisture content of avocado fruits were revealed. It has been established that visualization upon avocado inline sorting by moisture is possible when using factor loadings as pseudo-color. Calibration models for determination of moisture and dry matter of avocado fruits were built based on the data of moisture measurement and hyperspectral images. The matrix of spectral data was formed by two methods: random selection of spectral signatures of HSIs from the whole surface of fruits or the image surface of HSIs of fruits (initial HSIs) as a region of interest (ROI). Based on the data of moisture measurement and selection of spectral signatures of hyperspectral images, calibration models were built for detection of moisture and dry matter of avocado fruits. Using sequential simulation by the projection to latent structures (PLS) method, accurate calibration models were developed to detect moisture (RP2 = 0.89) and dry matter (RP2 = 0.92) in the composition of avocado fruits. When building calibration models by the initial HSIs, models were obtained to predict moisture (RС2 = 0.99) and dry matter (RС2 = 0.99) in the composition of avocado fruits. It is proposed to use calibration models by the initial HSIs to determine moisture and dry matter in the intervals of the acceptable values according to the acting standard UNECE STANDARD FFV-42:2019.

Quantitative systems pharmacology holds the promises of integrating results from laboratory animals or in vitro human systems into the design of human pharmacokinetic/pharmacodynamic (PK/PD) models allowing for precision and personalized medicine. However, reliable and general in vitro‐to‐in vivo extrapolation and interspecies scaling methods are still lacking. Here, we developed a translational strategy for the anticancer drug oxaliplatin. Using ex vivo PK data in the whole blood of the mouse, rat, and human, a model representing the amount of platinum (Pt) in the plasma and in the red blood cells was designed and could faithfully fit each dataset independently. A “purely physiologically‐based (PB)” scaling approach solely based on preclinical data failed to reproduce human observations, which were then included in the calibration. Investigating approaches in which one parameter was set as species‐specific, whereas the others were computed by PB scaling laws, we concluded that allowing the Pt binding rate to plasma proteins to be species‐specific permitted to closely fit all data, and guaranteed parameter identifiability. Such a strategy presenting the drawback of including all clinical datasets, we further identified a minimal subset of human data ensuring accurate model calibration. Next, a “whole body” model of oxaliplatin human PK was inferred from the ex vivo study. Its three remaining parameters were estimated, using one third of the available patient data. Remarkably, the model achieved a good fit to the training dataset and successfully reproduced the unseen observations. Such validation endorsed the legitimacy of our scaling methodology calling for its testing with other drugs.

Modeling in systems and synthetic biology relies on accurateparameterestimates and predictions. Accurate model calibration relies, in turn,on data and on how well suited the available data are to a particularmodeling task. Optimal experimental design (OED) techniques can beused to identify experiments and data collection procedures that willmost efficiently contribute to a given modeling objective. However,implementation of OED is limited by currently available software toolsthat are not well suited for the diversity of nonlinear models andnon-normal data commonly encountered in biological research. Moreover,existing OED tools do not make use of the state-of-the-art numericaltools, resulting in inefficient computation. Here, we present theNLoed software package and demonstrate its use with in vivo data froman optogenetic system in Escherichia coli. NLoed is an open-source Python library providing convenient accessto OED methods, with particular emphasis on experimental design forsystems biology research. NLoed supports a wide variety of nonlinear,multi-input/output, and dynamic models and facilitates modeling anddesign of experiments over a wide variety of data types. To supportOED investigations, the NLoed package implements maximum likelihoodfitting and diagnostic tools, providing a comprehensive modeling workflow.NLoed offers an accessible, modular, and flexible OED tool set suitedto the wide variety of experimental scenarios encountered in systemsbiology research. We demonstrate NLoed’s capabilities by applyingit to experimental design for characterization of a bacterial optogeneticsystem.

Non-invasive flesh firmness prediction using near infrared spectroscopy has been perfected and validated on three apple varieties. Three novel calibration models were developed following three year's of repeated large-scale sampling of stored commercial apple varieties ‘Gala’, ‘Red Jonaprince’ and ‘Jonagored’. The spectroscopic results were compared directly with those obtained using the invasive method. Increased accuracy of calibration models was achieved with the newly established data collection model. The results exhibited coefficient of determination for calibration, R2, and ratio of prediction to deviation (RPD) in excess of 0.91 and 2.3, respectively, thus enabling excellent prediction of flesh firmness via a non-invasive and fast spectroscopic approach. The highest R2 obtained was 0.94, RPD 2.6, root mean square error of calibration 5.87 N, and root mean square error of cross-validation (internal) 6.75 N for variety ‘Red Jonaprince’. Our complex long-term study provided excellent external validated calibration models and the approach can help developing calibration models for commercial sorting lines using near infrared spectroscopy.