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Network real-time kinematic (NRTK) coverage is defined as the area inside a station network. In conventional NRTK, the distance between stations is limited to 100 km, thus restricting the coverage of NRTK. In this study, we propose the utilization of an ionospheric-free combination and the application of a Kriging weighting model to mitigate tropospheric delay to extend the coverage of NRTK through network expansion. A network with station distances exceeding 100 km was constructed, and the residual errors, along with the success-fix rate of integer ambiguities, were analyzed on both sunny and rainy days to confirm the potential for network expansion using the proposed method. The results confirm that the success-fix rate increased by up to 44.3% on rainy days, compared with that of the traditional interpolation method. Furthermore, a high level of performance in integer ambiguity resolution can be maintained within the expanded network, regardless of the weather conditions. [ABSTRACT FROM AUTHOR]

The turbulent ionosphere causes phase shifts to incoming radio waves on a broad range of temporal and spatial scales. When an interferometer is not sufficiently calibrated for the direction-dependent ionospheric effects, the time-varying phase shifts can cause the signal to decorrelate. The ionosphere's influence over various spatiotemporal scales introduces a baseline-dependent effect on the interferometric array. We study the impact of baseline-dependent decorrelation on high-redshift observations with the Low Frequency Array (LOFAR). Data sets with a range of ionospheric corruptions are simulated using a thin-screen ionosphere model, and calibrated using the state-of-the-art LOFAR epoch of reionization pipeline. For the first time ever, we show the ionospheric impact on various stages of the calibration process including an analysis of the transfer of gain errors from longer to shorter baselines using realistic end-to-end simulations. We find that direction-dependent calibration for source subtraction leaves excess power of up to two orders of magnitude above the thermal noise at the largest spectral scales in the cylindrically averaged autopower spectrum under normal ionospheric conditions. However, we demonstrate that this excess power can be removed through Gaussian process regression, leaving no excess power above the 10 per cent level for a |$5~$| km diffractive scale. We conclude that ionospheric errors, in the absence of interactions with other aggravating effects, do not constitute a dominant component in the excess power observed in LOFAR epoch of reionization observations of the North Celestial Pole. Future work should therefore focus on less spectrally smooth effects, such as beam modelling errors. [ABSTRACT FROM AUTHOR]

This work is focused on finding G‐optimal designs theoretically for kriging models with two‐dimensional inputs and separable exponential covariance structures. For design comparison, the notion of evenness of two‐dimensional grid designs is developed. The mathematical relationship between the design and the supremum of the mean squared prediction error (SMSPE) function is studied and then optimal designs are explored for both prospective and retrospective design scenarios. In the case of prospective designs, the new design is developed before the experiment is conducted and the regularly spaced grid is shown to be the G‐optimal design. Retrospective designs are constructed by adding or deleting points from an already existing design. Deterministic algorithms are developed to find the best possible retrospective designs (which minimizes the SMSPE). It is found that a more evenly spread design under the G‐optimality criterion leads to the best possible retrospective design. For all the cases of finding the optimal prospective designs and the best possible retrospective designs, both frequentist and Bayesian frameworks have been considered. The proposed methodology for finding retrospective designs is illustrated with a spatiotemporal river water quality monitoring experiment. [ABSTRACT FROM AUTHOR]

Machine learning as a data‐driven solution has been widely applied in the field of fatigue lifetime prediction. In this paper, three models for wideband fatigue life prediction are built based on three machine learning models, that is, support vector regression (SVR), Gaussian process regression (GPR), and artificial neural network (ANN). All the three prediction models use the parameter b of the well‐known Tovo–Benasciutti (TB) model as their outputs to realize fatigue life prediction and their generalization abilities are enhanced by employing numerous power spectrum samples with different bandwidth parameters and a variety of material properties related to fatigue life. Sufficient Monte Carlo numerical simulations demonstrate that the newly developed machine learning models are superior to the traditional frequency‐domain models in terms of life prediction accuracy and the ANN model has the best overall performance among the three developed machine learning models. Highlights: Three frequency‐domain models are devised based on machine learning.SVR, GPR, and ANN models are employed for random fatigue life predictions.Three models are suitable for predicting fatigue life under wideband random loads.Three models outperform existing frequency‐domain models in prediction accuracy. [ABSTRACT FROM AUTHOR]

The red-billed blue magpie optimizer (RBMO) is employed in this research study to address parameter extraction in polymer exchange membrane fuel cells (PEMFCs), along with three recently implemented optimizers. The sum of squared deviations (SSD) between the simulated and measured stack voltages defines the fitness function of the optimization problem under investigation subject to a set of working constraints. Three distinct PEMFCs stacks models—the Ballard Mark, Temasek 1 kW, and Horizon H-12 units—are used to illustrate the applied RBMO's feasibility in solving this challenge in comparison to other recent algorithms. The highest percentages of biased voltage per reading for the Ballard Mark V, Temasek 1 kW, and Horizon H-12 are, respectively, +0.65%, +0.20%, and −0.14%, which are negligible errors. The primary characteristics of PEMFC stacks under changing reactant pressures and cell temperatures are used to evaluate the precision of the cropped optimized parameters. In the final phase of this endeavor, the sensitivity of the cropped parameters to the PEMFCs model's performance is investigated using two machine learning techniques, namely, artificial neural network and Gaussian process regression models. The simulation results demonstrate that the RBMO approach extracts the PEMFCs' appropriate parameters with high precision. [ABSTRACT FROM AUTHOR]

In near coastal environments, nautical charts provide crucial information for navigation and routing both in real-time operations and during planning stages. The cost of data collection as well as capacity constraints in the processing pipeline make reliable bathymetric information in such areas sparse. Prioritization rules can help guide the efforts to where information is the most valuable. AIS data provide accounts of real ship movements, indicating both desirable paths and minimum depths. We propose a statistical model for combining sparse bathymetric soundings with AIS observations for improved prediction of depths for generation of feasible transportation corridors. The method relies on viewing AIS draughts as censored observations of the true depth. A case-study is performed for the southern archipelago of Gothenburg using the program R-INLA. The non-stationarity caused by having boundaries with known (zero) depth and holes (land) in the domain is handled through discretization. Varying amounts of AIS data, ranging from none to 1824 observations, are used in the experiments. Results show predicted depths within the range of data values, and that inclusion of AIS data serve to push the field down to ensure that traverseable areas are predicted as such revealing corridors in narrow passages where bathymetric soundings are lacking. [ABSTRACT FROM AUTHOR]

Evaluating the reliability of deep soft rock tunnels is a very important issue to be solved. In this study, we propose a Monte Carlo simulation reliability analysis method (MCS–RAM) integrating the adaptive momentum stochastic optimization algorithm (Adam), Bayesian inference theory and Gaussian process regression (GPR) with combined kernel function, and we developed it in Python. The proposed method used the Latin hypercube sampling method to generate a dataset sample of geo-mechanical parameters, constructed combined kernel functions of GPR and used GPR to establish a surrogate model of the nonlinear mapping relationship between displacements and mechanical parameters of the surrounding rock. Adam was used to optimize the hyperparameters of the surrogate model. The Bayesian inference algorithm was used to obtain the probability distribution of geotechnical parameters and the optimal surrounding rock mechanical parameters. Finally, the failure probability was computed using MCS–RAM based on the optimized surrogate model. Through the application of an engineering case, the results indicate that the proposed method has fewer prediction errors and stronger prediction ability than Kriging or XGBoost, and it can significantly save computational time compared with the traditional polynomial response surface method. The proposed method can be used in the reliability analysis of all shapes of tunnels. [ABSTRACT FROM AUTHOR]

This study examines the challenges related to the storage and distribution of cabin materials on board during the construction of cruise ships. The construction process of cruise ships involves the assembly of various cabin materials, which are temporarily stored and transported in a complex deck environment with multiple obstacles. These processes can be viewed as location-routing problems (LRPs). However, existing solutions for LRPs do not account for the obstacles commonly encountered in deck environments. Therefore, this paper introduces a new variant of LRPs, termed as the BE-LRP (blocking environment location-routing problem). Initially, by considering the optimization objectives of minimizing the distribution distance and the number of vehicles required, the paper develops an optimization model for the location-routing of on-board materials while taking obstacles into consideration. Subsequently, a method for estimating blocked distances is proposed. This method utilizes Gaussian process regression to predict blocked distances and enables the rapid estimation of distances obstructed by obstacles between different destinations. Lastly, a hybrid obstacle blocking distance processing HO-NSGAII algorithm is formulated to address the BE-LRP. Experimental comparisons demonstrate that the distribution scheme derived from this research method does not necessitate modifications to the delivery distance when compared to the traditional NSGAII algorithm, and better aligns with the actual deck conditions. With an equivalent number of delivery vehicles, the scheme results in shorter delivery distances. The delivery distance can be reduced by 3–17% under varying circumstances. These findings are advantageous for enhancing the efficiency of cruise ship construction and mitigating the impact of warehousing and distribution on construction progress. [ABSTRACT FROM AUTHOR]

This study aims to improve the accuracy of bathymetry predicted by gravity-geologic method (GGM) using the optimal machine learning model selected from machine learning techniques. In this study, several machine learning techniques were utilized to determine the optimal model from the performance of depth and gravity anomalies. In addition, a tuning density contrast calculated from satellite altimetry-derived free-air gravity anomalies (FAGAs) was applied to estimate enhanced bathymetry. By comparison with shipborne depth, the accuracy of the bathymetry estimated by using satellite altimetry-derived FAGAs and machine learning was evaluated. The findings reveal that the bathymetry predicted by the optimal machine learning using the Gaussian process regression and the GGM with a tuning density contrast can enhance the accuracy of 82.64 m, showing an improvement of 67.40% in the RMSE at shipborne depth measurements. Although the tuning density is larger than 1.67 g/cm3, bathymetry using satellite altimetry-derived FAGAs and machine learning can be effectively improved with higher accuracy. [ABSTRACT FROM AUTHOR]

Enhancing thermal efficiency and minimizing weight are prevailing issues in aero engines. Owing to its hollow structure, the twin-web turbine disc exhibits remarkable weight reduction properties, while its enhanced cooling constitutes a novel challenge. In this study, a twin-web turbine disc cavity system is numerically investigated. To enhance the cooling effect and minimize pressure loss, a multi-objective genetic algorithm and Kriging surrogate model are employed to optimize the radial height of the pre-swirl nozzle and receiver hole in the disc cavity system. The results indicate that the overall performance of Opt-3, derived from the Technique for Order Preference by Similarity to the Ideal Solution method within the Pareto frontier, is superior. This configuration achieves a uniform low distribution of rotor temperatures while maintaining moderate pressure losses. Notably, the maximum temperature is reduced by 21.1 K compared to the basic model, with pressure losses remaining largely unchanged. Additionally, an increase in the flow ratio leads to a reduction in both the maximum temperature and average temperature of the back web while simultaneously increasing the temperature of the front web and augmenting pressure losses. However, it is important to note that the degree of variation in these parameters diminishes with increasing flow ratios. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian inference framework for updating the structural rigidity ratio of aging hollow slab RC bridges using deflection measurements. The framework models the structural rigidity ratio as a stochastic field along the hollow RC slabs, using the Karhunen–Loeve (KL) transform to capture spatial correlation and variation. Bayesian inference is then applied using deflection data from static loading tests, supported by a finite element model (FEM) and a Kriging surrogate model to enhance computational efficiency. The posterior distribution of the structural rigidity ratio is derived using a Markov chain Monte Carlo (MCMC) sampler. The proposed method was tested on an RC bridge with hollow slabs, using deflection measurements taken before and after reinforcement. The Bayesian updates indicated increased structural rigidity ratios after reinforcement, validating the effectiveness of the reinforcement. The deflection predictions from the updated models closely matched the measurements, with the 95% confidence bounds encompassing most of the data. This demonstrates the method's validity and robustness in capturing the structural improvements post-reinforcement. [ABSTRACT FROM AUTHOR]

Machine learning (ML) approaches, widely used in civil engineering, have the potential to reduce computing costs and enhance predictive capabilities. However, many ML methods have yet to be applied to develop models that accurately analyze the nonlinear dynamic response of cross-fault hydraulic tunnels (CFHTs). To predict CFHT models and fragility curves effectively, we identify the most effective ML techniques and improve prediction capacity and accuracy by initially creating an integrated multivariate earthquake intensity measure (IM) from nine univariate earthquake IMs using principal component analysis. Structural reactions are then performed using incremental dynamic analysis by a multimedium-coupled interaction system. Four techniques are used to test ML–principal component analysis (PCA) feasibility. Meanwhile, mathematical statistical parameters are compared to standard probabilistic seismic demand models of expected and computed values using ML-PCA. Eventually, multiple stripe analysis–maximum likelihood estimation (MSA-MLE) is applied to assess the seismic performance of CFHTs. This study highlights that the Gaussian process regression and integrated IM can improve reliable probability and reduce uncertainties in evaluating the structural response. Thorough numerical analysis, using the suggested methodology, one can efficiently assess the seismic fragilities of the tunnel by the predicted model. ML-PCA techniques can be viewed as an alternate strategy for seismic design and CFHT performance enhancement in real-world engineering. [ABSTRACT FROM AUTHOR]

Mapping of soil nutrient parameters using experimental measurements and geostatistical approaches to assist site-specific fertiliser advisories is anticipated to play a significant role in Smart Agriculture. FarmerZone is a cloud service envisioned by the Department of Biotechnology, Government of India, to provide advisories to assist smallholder farmers in India in enhancing their overall farm production. As a part of the project, we evaluated the soil spatial variability of three potato agroecological zones in India and provided soil health cards along with field-specific fertiliser recommendations for potato cultivation to farmers. Specifically, 705 surface samples were collected from three representative potato-growing districts of Indian states (Meerut, UP; Jalandhar, Punjab and Lahaul and Spiti, HP) and analysed for soil parameters such as organic carbon, macronutrients (NPK), micronutrients (Zn, Fe, Mn, and Cu), pH, and EC. The soil parameters were integrated into a geodatabase and subjected to kriging interpolation to create spatial soil maps of the targeted potato agroecological zones through best-fit experimental semivariograms. The spatial distribution showed a deficiency of soil organic carbon in two studied zones and available nitrogen among all studied zones. The available phosphorus and potassium varied among the agroecological zones. The micronutrient levels were largely sufficient in all the zones except at a few specific sites where nutrient advisories are recommended to replenish. The general management strategies were recommended based on the nutrient status in the studied area. This study clearly supports the significance of site-specific soil analytics and interpolated spatial soil mapping over any targeted agroecological zones as a promising strategy to deliver reliable advisories of fertiliser recommendations for smart farming. [ABSTRACT FROM AUTHOR]

Accurately predicting cutting forces in hard turning processes can lead to improved process control, reduced tool wear, and enhanced productivity. This study aims to predict machining force components during the hard turning of AISI 52100 bearing steel using machine learning models. Specifically, eight models were considered, and their prediction performance was assessed using experimental data collected during AISI 52100 bearing steel turning with a CBN cutting tool. The fivefold cross-validation technique has been adopted in training to obtain more reliable estimates of the performance of a model and reduce the risk of overfitting the data. Results showed that the Gaussian process regression (GPR) and decision tree regression outperformed the other models, with averaged root-mean-square error values of 14.44 and 12.72, respectively. GPR also provided prediction uncertainty. Additionally, feature selection was performed using two algorithms, namely Regressional Relief-F and F test, to identify the most important features impacting the cutting forces. The findings of this study can be useful in optimizing cutting parameters for hard turning processes to select cutting forces, reduce tool wear, and minimize the generated heat during the machining process. [ABSTRACT FROM AUTHOR]

Background and Objectives Soil temperature is one of the important factors in agriculture and hydrology, and its accurate measurement is very important to ensure the optimal growth and development of the plants. Soil temperature is a factor that affects many processes such as seed germination, soil moisture level, aeration, nitrification and availability of plant nutrients. Because the soil temperature data is measured in some synoptic stations, most of the data have limitations or are incomplete. However, choosing the best method to estimate soil temperature with other available meteorological data is an efficient approach in many fields. Soil temperature depends on several factors including color, slope, vegetation, density, humidity and amount of sunlight. Currently, some physical models are available that are intrinsically related to the state of soil heat flow and energy balance in underlying soils to estimate soil temperature. The importance of soil temperature in agricultural sciences and hydrology, on the one hand, and the existence of many difficulties in recording this vital parameter, have led researchers to seek a relationship between soil temperature and other parameters in order to be able to estimate soil temperature with optimal accuracy. Methodology In this research, daily soil temperature values were collected during the time period of 1990-2022 in Ramsar, Arak and Shiraz stations. On the other hand, the parameters of minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tm), maximum relative humidity (Umax), minimum relative humidity (Umin), average relative humidity (Um), average wind speed (FFM) and Sunshine hours (SSHN) was considered as the input parameters and soil temperature (T-soil) as the target parameter. It is worth mentioning that the way of choosing different input compounds to estimate the value of soil temperature in the studied models is based on having a higher correlation with soil temperature based on the thermal map. Moreover, the ability of data-driven models of Gaussian process regression (GPR), support vector regression (SVR), M5P algorithm, linear regression (LR), and multilayer perceptron (MLP) neural network in estimating soil temperature was evaluated using different statistical parameters of correlation coefficient (R), root mean square error (RMSE), Nash Sutcliffe coefficient (NS), average absolute value of percentage error (MAPE) and Wilmot agreement index (WI). Findings The evaluation of five GPR, SVR, M5P, LR and MLP models for three stations of Arak, Ramsar and Shiraz shows that the 8th M5P scenario and the 8th LR scenario with lower root mean square error respectively (0.899 and 0.889) for Ramsar station, (0.958 and 0.949) for Arak station and (0.966 and 0.953) for Shiraz station have better performance than other studied models. Also, the evaluation of the impact of the input parameters in creating the scenario for the models shows that the parameters of relative humidity and air temperature had more important role than other input parameters. So that by adding parameters of relative humidity and air temperature, the accuracy of the model has increased. Therefore, these parameters are among the most key and important parameters of soil temperature. Conclusion The analysis and evaluation of soil characteristics has an important impact in the fields of hydrology, agriculture and climate. On the other hand, soil temperature has a direct relationship with the amount of moisture available to the plant, so that an increase in soil temperature can increase the transpiration rate of plants, and as a result, soil moisture decreases. Soil temperature is also an essential factor in agriculture because it determines whether plants can grow, and controls soil chemistry and biology and atmosphere-land gas exchange. Therefore, predicting soil temperature is very important for successful crop management and yield optimization. So, In this research, five data-driven methods of GPR, SVR, M5P, LR and MLP were used to predict soil temperature in Arak, Ramsar and Shiraz stations during the time period of 1990-2022. The obtained results were compared using statistical parameters and it was concluded that the 8th M5P scenario and the 8th LR scenario have shown the best performance in three stations with the lowest error compared to all scenarios. Therefore, the application of the mentioned models to predict the soil temperature has proper accuracy and is recommended for management and evaluation in terms of environmental and civil aspects. [ABSTRACT FROM AUTHOR]

Abstract With the proposal of intelligent mines, the demand for drilling is increasing daily. Therefore, it is particularly crucial to gather more geological data by interpolation of limited drilling data for subsequent three-dimensional geological modeling. In this paper, a hybrid sparrow optimization Kriging model (HSSA), in which chaos theory and Levy flight are integrated into the initial population update algorithm of the sparrow algorithm and the location update algorithm of the entrants, is proposed. Next, the golden sine optimization algorithm is introduced into the reconnaissance and early warning mechanism of the sparrow algorithm to further improve the accuracy and local escape ability. By the correlation optimization of the original sparrow algorithm, the speed and accuracy of swarm intelligence optimization are further improved. In addition, the model solves the parameters of the variation function of the ordinary Kriging interpolation and reduces the generation error of the formation data interpolation. The results of relevant experiments show that the hybrid sparrow optimization Kriging model improves the accuracy and convergence speed compared with other swarm intelligence algorithms and that the accuracy of this model is improved by 8.4% compared with the original Kriging interpolation algorithm. Based on the hybrid sparrow optimization Kriging model, we propose a three-dimensional stratigraphic model for the Yangchangwan Coal Mine, which provides further support for mining operations and three-dimensional stratigraphic research in this area. The accuracy and applicability of the hybrid sparrow optimization Kriging model are further explained using a case study with the stratigraphic model data in the Yangchangwan Coal Mine. HSSA with significant potential for applications in industries such as coal mining and geological exploration. In these fields, the efficient acquisition, processing, and modeling of stratigraphic data are critical for enhancing geological interpretation and optimizing operational workflows.

Clay is an essential resource for improving the brick and ceramic industries. However, the demand for clay needs to grow. Estimating industrial mineral reserves requires an optimal and unbiased statistical tool. Geographical information systems and geostatistics are two promising tools that could be used to predict clay resources. The study objective was to apply these two tools to compare the performance of the methods for spatial clay resource prediction, considering clay thickness and spatial distribution. The demonstration site is in Tam Binh district, Vinh Long Province, Viet Nam. The results show that the Gaussian variation model at a 600 × 400 m distance is good for sampling and extrapolating clay thickness. This is based on 240 geolocated samples with different densities. Projected clay reserves match measurements, and the spatial statistical method is more accessible and valuable than the traditional method. The interpolated method’s total selected soil borehole number was optimized for clay resource estimation, and geostatistics can help manage and plan clay resources and save money. Future research should examine various spatial interpolation techniques and approaches in management, land use, and topography to improve the accuracy of calculations.

Free energy simulations that employ combined quantum mechanical and molecular mechanical (QM/MM) potentials at ab initio QM (AI) levels are computationally highly demanding. Here, we present a machine-learning-facilitated approach for obtaining AI/MM-quality free energy profiles at the cost of efficient semiempirical QM/MM (SE/MM) methods. Specifically, we use Gaussian process regression (GPR) to learn the potential energy corrections needed for an SE/MM level to match an AI/MM target along the minimum free energy path (MFEP). Force modification using gradients of the GPR potential allows us to improve configurational sampling and update the MFEP. To adaptively train our model, we further employ the sparse variational GP (SVGP) and streaming sparse GPR (SSGPR) methods, which efficiently incorporate previous sample information without significantly increasing the training data size. We applied the QM-(SS)GPR/MM method to the solution-phase SN2 Menshutkin reaction, N H 3 + C H 3 C l → C H 3 N H 3 + + C l − , using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. For 4000 configurations sampled along the MFEP, the iteratively optimized AM1-SSGPR-4/MM model reduces the energy error in AM1/MM from 18.2 to 4.4 kcal/mol. Although not explicitly fitting forces, our method also reduces the key internal force errors from 25.5 to 11.1 kcal/mol/ A ̊ and from 30.2 to 10.3 kcal/mol/ A ̊ for the N–C and C–Cl bonds, respectively. Compared to the uncorrected simulations, the AM1-SSGPR-4/MM method lowers the predicted free energy barrier from 28.7 to 11.7 kcal/mol and decreases the reaction free energy from −12.4 to −41.9 kcal/mol, bringing these results into closer agreement with their AI/MM and experimental benchmarks. [ABSTRACT FROM AUTHOR]

We present a new program implementation of the Gaussian process regression adaptive density-guided approach [Schmitz et al., J. Chem. Phys. 153, 064105 (2020)] for automatic and cost-efficient potential energy surface construction in the MidasCpp program. A number of technical and methodological improvements made allowed us to extend this approach toward calculations of larger molecular systems than those previously accessible and maintain the very high accuracy of constructed potential energy surfaces. On the methodological side, improvements were made by using a Δ-learning approach, predicting the difference against a fully harmonic potential, and employing a computationally more efficient hyperparameter optimization procedure. We demonstrate the performance of this method on a test set of molecules of growing size and show that up to 80% of single point calculations could be avoided, introducing a root mean square deviation in fundamental excitations of about 3 cm−1. A much higher accuracy with errors below 1 cm−1 could be achieved with tighter convergence thresholds still reducing the number of single point computations by up to 68%. We further support our findings with a detailed analysis of wall times measured while employing different electronic structure methods. Our results demonstrate that GPR-ADGA is an effective tool, which could be applied for cost-efficient calculations of potential energy surfaces suitable for highly accurate vibrational spectra simulations. [ABSTRACT FROM AUTHOR]

The theoretical investigation of gas adsorption, storage, separation, diffusion, and related transport processes in porous materials relies on a detailed knowledge of the potential energy surface of molecules in a stationary environment. In this article, a new algorithm is presented, specifically developed for gas transport phenomena, which allows for a highly cost-effective determination of molecular potential energy surfaces. It is based on a symmetry-enhanced version of Gaussian process regression with embedded gradient information and employs an active learning strategy to keep the number of single point evaluations as low as possible. The performance of the algorithm is tested for a selection of gas sieving scenarios on porous, N-functionalized graphene and for the intermolecular interaction of CH4 and N2. [ABSTRACT FROM AUTHOR]

We present a local and transferable machine-learning approach capable of predicting the real-space density response of both molecules and periodic systems to homogeneous electric fields. The new method, Symmetry-Adapted Learning of Three-dimensional Electron Responses (SALTER), builds on the symmetry-adapted Gaussian process regression symmetry-adapted learning of three-dimensional electron densities framework. SALTER requires only a small, but necessary, modification to the descriptors used to represent the atomic environments. We present the performance of the method on isolated water molecules, bulk water, and a naphthalene crystal. Root mean square errors of the predicted density response lie at or below 10% with barely more than 100 training structures. Derived polarizability tensors and even Raman spectra further derived from these tensors show good agreement with those calculated directly from quantum mechanical methods. Therefore, SALTER shows excellent performance when predicting derived quantities, while retaining all of the information contained in the full electronic response. Thus, this method is capable of predicting vector fields in a chemical context and serves as a landmark for further developments. [ABSTRACT FROM AUTHOR]

The increasing popularity of machine learning (ML) approaches in computational modeling, most prominently ML interatomic potentials, opened possibilities that were unthinkable only a few years ago—structure and dynamics for systems up to many thousands of atoms at an ab initio level of accuracy. Strictly referring to ML interatomic potentials, however, a number of modeling applications are out of reach, specifically those that require explicit electronic structure. Hybrid ("gray box") models based on, e.g., approximate, semi-empirical ab initio electronic structure with the aid of some ML components offer a convenient synthesis that allows us to treat all aspects of a certain physical system on the same footing without targeting a separate ML model for each property. Here, we compare one of these [Density Functional Tight Binding with a Gaussian Process Regression repulsive potential (GPrep-DFTB)] with its fully "black box" counterpart, the Gaussian approximation potential, by evaluating performance in terms of accuracy, extrapolation power, and data efficiency for the metallic Ru and oxide RuO2 systems, given exactly the same training set. The accuracy with respect to the training set or similar chemical motifs turns out to be comparable. GPrep-DFTB is, however, slightly more data efficient. The robustness of GPRep-DFTB in terms of extrapolation power is much less clear-cut for the binary system than for the pristine system, most likely due to imperfections in the electronic parametrization. [ABSTRACT FROM AUTHOR]

We present a machine learning approach to calculating electronic specific heat capacities for a variety of benchmark molecular systems. Our models are based on data from density matrix quantum Monte Carlo, which is a stochastic method that can calculate the electronic energy at finite temperature. As these energies typically have noise, numerical derivatives of the energy can be challenging to find reliably. In order to circumvent this problem, we use Gaussian process regression to model the energy and use analytical derivatives to produce the specific heat capacity. From there, we also calculate the entropy by numerical integration. We compare our results to cubic splines and finite differences in a variety of molecules in which Hamiltonians can be diagonalized exactly with full configuration interaction. We finally apply this method to look at larger molecules where exact diagonalization is not possible and make comparisons with more approximate ways to calculate the specific heat capacity and entropy. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the spatial variation of some soil properties such as soil texture, organic carbon content, soil pH and electrical conductivity (EC) using geostatistical methods in Margon town, Kohgiluyeh and Boyer-Ahmad province, Iran. For this purpose, 58 surface soil samples (0-30 cm depth) were prepared from the studied area and some physical and chemical characteristics of the soils were measured. The studied properties were estimated by various interpolation methods including ordinary kriging, simple kriging, disjunctive kriging, co-kriging, and inverse distance weighting (IDW) using GIS software. The capability of these methods was evaluated by the mean absolute error (MAE), and the root mean square error (RMSE). Based on the absolute mean error and the squared mean squared error, the spherical model for pH, salinity, sand percentage and soil silt percentage and the exponential model for clay and organic carbon percentage had the highest accuracy. Moreover, the results indicated that conventional co-kriging method for organic carbon percentage content and soil pH, simple co-kriging method for clay and silt percentage, simple kriging method for soil EC, and conventional kriging method for sand percentage were the most suitable methods as compared to the other studied methods.

ABSTRACTUrban footpaths are the main outdoor activity spaces for pedestrians. It is traditionally believed that the air temperature of a pedestrian walkway under tree shade is lower. However, from the perspective of vertical air temperature profiles, there is little difference in air temperatures between a shaded pedestrian walkway and one under unshaded sunlight. This paper adopts a human-centred approach to investigate the three-dimensional spatial distribution of air temperatures in a pedestrian walkway in Taipei City under tree shade. It analyzes the air temperature reduction caused by attenuation of ground reflected long-wave radiation due to shade, and the effect of shade on environmental ventilation. It was found that air temperatures varied along pedestrian walkway height and direction. The measured data shows that the main difference in air temperature between shaded areas and areas exposed to sunlight is in the 0–0.5 m height range, and there is little difference in air temperature between the 0.5 m-2 m height range.Higher shades led to poorer ventilation and were more likely to cause local overheating. This suggests the need to assess urban pedestrian thermal comfort from a three-dimensional perspective, considering not just planar shades but also vertical shading and ventilation.

The ISO 17450-2:2012 defines the verification operation. The ISO 17450-1:2011 defines the extracted features and the extraction operations on the "non-ideal surface model or on the real surface of the workpiece". The ISO 12781-2:2011 and 12180-2:2011 provide dedicated extraction strategies for the flatness and cylindricity. But no real measurement process is explicit here. However, one of the key factors in an inspection plan is the choice of the measurement strategy. Some sampling strategies, such as Hammersley sequence, offer robust and efficient technics to ensure good distribution of measurement points. But measurements are always defined with a "blind sampling strategy". So, in this paper, the author proposed to use adaptative sampling strategies, based on the Active learning method based on Kriging for the Inspection of Large Surfaces (AK-ILS) and developed an original Active Learning method based on Multi-Layer-Perceptron (AL-MLP), to classify the conform / non-conform parts and to ensure the reconstruction of the surfaces, with smart Skin Models Shapes and a minimal sampling size. [ABSTRACT FROM AUTHOR]

The 3D ionosphere structure is of interest in many fields such as radio frequency communication and global navigation satellite system (GNSS) applications. However, the limited temporal and spatial coverage of measurements poses a challenge for 3D electron density modeling. To overcome this challenge, we explore the use of kriging interpolation technique. The kriging interpolation is performed to obtain 3D representation of the ionosphere over electron density measurements retrieved by GNSS radio-occultation (RO) data. RO measurements are first reduced to "shape function," the ratio of electron density to vertical total electron content (VTEC), aiming to create a background model. Then, the empirical residual semivariogram is analyzed for variation characteristics of the shape functions under different solar geomagnetic conditions. Finally, 3D kriging is adopted for shape function interpolation. Compared to the modeling results without kriging, the maximum root mean square error (RMSE) reduction reaches 3.4 × 10 - 4 km - 1 , which amounts to 3.4 × 10 11 el/m 3 of electron density when VTEC is assumed as 100 TECU. This improvement accounts for 17.8% of root mean square (RMS) of shape function. [ABSTRACT FROM AUTHOR]

Given the high costs of soil sampling, low and extra-low sampling densities are still being used. Low-density soil sampling usually does not allow the computation of experimental variograms reliable enough to fit models and perform interpolation. In the absence of geostatistical tools, deterministic methods such as inverse distance weighting (IDW) are recommended but they are susceptible to the "bull's eye" effect, which creates non-smooth surfaces. This study aims to develop and assess interpolation methods or approaches to produce soil test maps that are robust and maximize the information value contained in sparse soil sampling data. Eleven interpolation procedures, including traditional methods, a newly proposed methodology, and a kriging-based approach, were evaluated using grid soil samples from four fields located in Central Alberta, Canada. In addition to the original 0.4 ha⋅sample−1 sampling scheme, two sampling design densities of 0.8 and 3.5 ha⋅sample−1 were considered. Among the many outcomes of this study, it was found that the field average never emerged as the basis for the best approach. Also, none of the evaluated interpolation procedures appeared to be the best across all fields, soil properties, and sampling densities. In terms of robustness, the proposed kriging-based approach, in which the nugget effect estimate is set to the value of the semi-variance at the smallest sampling distance, and the sill estimate to the sample variance, and the IDW with the power parameter value of 1.0 provided the best approaches as they rarely yielded errors worse than those obtained with the field average. [ABSTRACT FROM AUTHOR]

This study aimed to investigate and analyze the performance of twelve machine learning (ML) algorithms in estimating the construction time and cost of drill and blast tunnels. Thirteen tunnels located in different regions of Iran were selected, resulting in 900 data sets. Ten parameters were identified as influential factors affecting the construction time and cost of these tunnels. 80% of the data set was used for training, while the remaining 20% was reserved for testing. Additionally, 288 unseen data sets were utilized for evaluation purposes. All the algorithms demonstrated accurate performance on the test data sets, with R-squared values exceeding 0.93. However, only the Gaussian process regression algorithm achieved satisfactory results on the unseen data sets. Furthermore, a graphical user interface (GUI) was developed based on the trained ML models. This GUI allows real-time estimation of the time and cost of drill and blast tunnels and can be updated during construction. [ABSTRACT FROM AUTHOR]

Microscopic modeling of vehicle movements and interactions is pivotal in traffic flow theory. Physics-based car-following (CF) models using mathematical formulations can delineate driving behavior in various traffic conditions with decent interpretability. However, given predetermined mathematical forms, they might fail to characterize complex, highly nonlinear phenomena. Data-driven CF models naturally excel in this regard considering their flexible architectures, but their performance is subject to data quality, especially distribution bias. In this paper, we propose a novel physics-informed particle filter (PIPF) model that fuses and takes advantage of the two approaches. Utilizing the intelligent driver model as the physics-based model and the multioutput Gaussian process regression as the data-driven model, the PIPF model integrates and embeds both models into a particle filter framework, enhancing both model adaptability and accuracy. The performance of the proposed model is examined through both single vehicle and multivehicle numerical experiments using the NGSIM trajectory data set. Compared with physics-based and data-driven models alone, the PIPF model demonstrates a performance improvement in terms of the root mean square error of about 11.16% and 29.43% in scenarios characterized by sparse data and about 19.81% and 3.84% in scenarios with sufficient data. Compared to traditional particle filtering models, the number of particles to achieve optimal results is reduced by 20%, meaning less computational complexity. [ABSTRACT FROM AUTHOR]

Irrigation is important for the growth of world agriculture, as it enables greater security in agricultural production. The use of center-pivot for irrigation is very common in Brazil. However, there are some information gaps, mainly related to spatial variability in the water application in variable topography. Thus, the present study aimed to apply the geostatistical approach to characterize and evaluate the magnitude of the pressure head (PH) spatial variability in center-pivot lateral lines operating in plots with variable topography. For this analysis, six different points were installed along the lateral line and measured with PH transducers in 18 lateral line angular positions in the study area. Universal kriging (UK) was used to estimate PH across the whole field. The semivariogram was adjusted by the hole effect theoretical model, indicating a strong spatial dependence on PH. A decision support system tool was developed to assist in the analysis of the PH spatial distribution along the center-pivot lateral line using a geostatistical approach (kriging). The proposed tool can be useful for managers of irrigable areas and to identify zones with high energy use (wasted PH) along lateral lines of center-pivot systems. The estimation of PH distribution using geostatistical and UK techniques was satisfactory, allowing the creation of a thematic map. Precision irrigation and monitoring using a thematic map of PH distribution from kriging can help monitor the operating conditions of a center-pivot, as well as improve the decision-making regarding proper management of the whole irrigation system. Practical Applications: Center-pivot irrigation systems are very common in agricultural production areas around the world. Due to the large area irrigated by center-pivots, it is very important to study the reduction in water and energy use in these equipment. Thus, an important point to be studied is the PH distribution along the center-pivot lateral line in areas with undulating topography, where there is a significant PH variation. In this study, we present an analysis of the PH distribution along the lateral line using a geostatistical approach (kriging) to obtain PH values in the entire irrigated area. For this, PH data collected at six different points along the lateral line were used in a decision support system tool, developed to estimate PH in the entire irrigated area using the geostatistical approach. This estimate was satisfactory, allowing the creation of a thematic map. The proposed tool proved to be useful for easily identifying zones with high energy use (wasted PH). In this way, the use of precision irrigation and the analysis of the equipment through thematic mapping can help irrigators in monitoring the operating conditions of a center-pivot. [ABSTRACT FROM AUTHOR]

Injection molding is a pivotal industrial process renowned for its high production speed, efficiency, and automation. Controlling the motion speed of injection molding machines is a crucial factor that influences production processes, directly affecting product quality and efficiency. This paper aims to tackle the challenge of achieving optimal tracking control of injection speed in a standard class of injection molding machines (IMMs) characterized by nonlinear dynamics. To achieve this goal, we propose a learning-based model predictive control (LMPC) scheme that incorporates Gaussian process regression (GPR) to predict and model uncertainty in the injection molding process (IMP). Specifically, the scheme formulates a nonlinear tracking control problem for injection speed, utilizing a GPR-based learning residual model to capture uncertainty and provide accurate predictions. It learns the dynamics model and historical data of the IMM, automatically adjusting the injection speed according to target requirements for optimal production control. Additionally, the optimization problem is efficiently solved using a control-constrained differential dynamic programming approach. Finally, we conduct comprehensive numerical experiments to demonstrate the effectiveness and efficiency of the proposed LMPC scheme for controlling injection speed in IMP. [ABSTRACT FROM AUTHOR]

Simulations of exciton and charge hopping in amorphous organic materials involve numerous physical parameters. Each of these parameters must be computed from costly ab initio calculations before the simulation can commence, resulting in a significant computational overhead for studying exciton diffusion, especially in large and complex material datasets. While the idea of using machine learning to quickly predict these parameters has been explored previously, typical machine learning models require long training times, which ultimately contribute to simulation overheads. In this paper, we present a new machine learning architecture for building predictive models for intermolecular exciton coupling parameters. Our architecture is designed in such a way that the total training time is reduced compared to ordinary Gaussian process regression or kernel ridge regression models. Based on this architecture, we build a predictive model and use it to estimate the coupling parameters which enter into an exciton hopping simulation in amorphous pentacene. We show that this hopping simulation is able to achieve excellent predictions for exciton diffusion tensor elements and other properties as compared to a simulation using coupling parameters computed entirely from density functional theory. This result, along with the short training times afforded by our architecture, shows how machine learning can be used to reduce the high computational overheads associated with exciton and charge diffusion simulations in amorphous organic materials. [ABSTRACT FROM AUTHOR]

We introduce Gaussian Process Regression (GPR) as an enhanced method of thermodynamic extrapolation and interpolation. The heteroscedastic GPR models that we introduce automatically weight provided information by its estimated uncertainty, allowing for the incorporation of highly uncertain, high-order derivative information. By the linearity of the derivative operator, GPR models naturally handle derivative information and, with appropriate likelihood models that incorporate heterogeneous uncertainties, are able to identify estimates of functions for which the provided observations and derivatives are inconsistent due to the sampling bias that is common in molecular simulations. Since we utilize kernels that form complete bases on the function space to be learned, the estimated uncertainty in the model takes into account that of the functional form itself, in contrast to polynomial interpolation, which explicitly assumes the functional form to be fixed. We apply GPR models to a variety of data sources and assess various active learning strategies, identifying when specific options will be most useful. Our active-learning data collection based on GPR models incorporating derivative information is finally applied to tracing vapor–liquid equilibrium for a single-component Lennard-Jones fluid, which we show represents a powerful generalization to previous extrapolation strategies and Gibbs–Duhem integration. A suite of tools implementing these methods is provided at https://github.com/usnistgov/thermo-extrap. [ABSTRACT FROM AUTHOR]

The positions of grid points for representing a multidimensional potential energy surface (PES) have a non-negligible impact on its accuracy and the associated computational effort for its generation. Six different positioning schemes were studied for PESs represented by n-mode expansions as needed for the accurate calculation of anharmonic vibrational frequencies by means of vibrational configuration interaction theory. A static approach, which has successfully been used in many applications, and five adaptive schemes based on Gaussian process regression have been investigated with respect to the number of necessary grid points and the accuracy of the fundamental modes for a small set of test molecules. A comparison with a related, more sophisticated, and consistent approach by Christiansen et al. is provided. The impact of the positions of the ab initio grid points is discussed for multilevel PESs, for which the computational effort of the individual electronic structure calculations decreases for increasing orders of the n-mode expansion. As a result of that, the ultimate goal is not the maximal reduction of grid points but rather the computational cost, which is not directly related. [ABSTRACT FROM AUTHOR]

The purpose of this work is to analyze and validate the gravimetric information in the study area in order to highlight the valid data for mapping the gravity anomaly from a grid generated by interpolation. In this context, we have performed a first step of data validation using the Kriging interpolation, this method was applied to a set of free-air anomalies reduced by the EGM08 geopotential model, from the test zone located in the north of Algeria, but the data were considered as values of an isotropic variable whose the variographic analysis depends only on the distances between points. In the second step the anisotropic character was taken into account, where the main tasks were to calculate the directional variograms for validating data and to perform interpolation by directional models. Finally, we have performed a computer application for interpolation and validation of gravimetric data by Kriging method, based on the algorithm for validation and grid generation of free-air anomalies, proposed in this article, where the results showed that the free-air anomaly is an isotropic variable which only depends on the distances between positions, and that the values resulting from interpolation are validated by the BGI grid, where the two maps generated one by interpolation and the other by the BGI grid are very similar to confirm the validity of the Kriging algorithm developed in this context.

AbstractData transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This article introduces a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach directly targets the posterior distribution of the transformation by linking it with the marginal distributions of the independent and dependent variables, and then deploys a Bayesian nonparametric model via the Bayesian bootstrap. Crucially, this approach delivers (a) joint posterior consistency under general conditions, including multiple model misspecifications, and (b) efficient Monte Carlo (not Markov chain Monte Carlo) inference for the transformation and all parameters for important special cases. These tools apply across a variety of data domains, including real-valued, positive, and compactly-supported data. Simulation studies and an empirical application demonstrate the effectiveness and efficiency of this strategy for semiparametric Bayesian analysis with linear models, quantile regression, and Gaussian processes. The R package SeBR is available on CRAN. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Numerous hydrological applications, such as soil erosion estimation, water resource management, and rain driven damage assessment, demand accurate and reliable rainfall erosivity data. However, the scarcity of gauge rainfall records and the inherent uncertainty in satellite and reanalysis-based rainfall datasets limit rainfall erosivity assessment globally. Here, we present a new global rainfall erosivity dataset (0.1° × 0.1° spatial resolution) integrating satellite (CMORPH and IMERG) and reanalysis (ERA5-Land) derived rainfall erosivity estimates with gauge rainfall erosivity observations collected from approximately 6,200 locations across the globe. We used a machine learning-based Gaussian Process Regression (GPR) model to assimilate multi-source rainfall erosivity estimates alongside geoclimatic covariates to prepare a unified high-resolution mean annual rainfall erosivity product. It has been shown that the proposed rainfall erosivity product performs well during cross-validation with gauge records and inter-comparison with the existing global rainfall erosivity datasets. Furthermore, this dataset offers a new global rainfall erosivity perspective, addressing the limitations of existing datasets and facilitating large-scale hydrological modelling and soil erosion assessments. [ABSTRACT FROM AUTHOR]

The incorporation of carbon nanotubes (CNTs) in concrete can improve the physical, mechanical, and durability properties. However, the interaction of CNTs with concrete and their effect on the mechanical properties remains a challenging issue. Also, the determination of mechanical properties through experimental testing is time‐consuming, laborious, and uneconomical. This study focuses on the development of machine learning (ML) models for the prediction of the mechanical properties of concrete. A comprehensive data set of 758 CNT‐modified concrete specimens was established for the compressive strength (CS), split tensile strength (STS), flexural strength (FS), and modulus of elasticity (MOE) values from the experimental studies in the literature. Afterward, the predictive models were developed using multilinear regression (MLR), support vector machine (SVM), ensemble methods (EN), regression tree (RT), and Gaussian process regression (GPR). It was found that among ML models, the GPR model predicted the CS, STS, and FS at the highest efficiency with the coefficient of determination (R2) of 0.83, 0.78, and 0.93, respectively while the performance of the SVM model was superior for predicting MOE with an R2 value of 0.91. The mean absolute error (MAE) of the GPR model for CS, STS, FS, and MOE were 2.92, 0.26, 0.35, and 1.31, respectively which were also lesser than other models. The training time of different models demonstrated that the GPR model has also a lower training time (~3 s) as compared to other models which indicates it has a high accuracy‐to‐time cost ratio. Further, the most influential parameters on CS were age, cement, water–cement ratio, and carbon nanotubes. The one‐way partial dependence analysis showed a direct correlation for age and cement but an inverse correlation for the water–cement ratio and fine aggregate. The graphical user interface provides the implication of the developed models for practical applications. [ABSTRACT FROM AUTHOR]

This paper aims to apply data-driven maximum power point tracking (MPPT) techniques specifically tailored for fuel cell vehicle (FCV) supported hybrid DC microgrids to enhance the power harvesting capability of fuel cell (FC) stacks. Compared to existing MPPT techniques, the current study focuses on developing and evaluating data-driven approaches for maximum power extraction by dynamically determining the operating point of FC power sources through a Zeta converter. An in-depth analysis is conducted by considering parameters such as efficiency, tracking accuracy, response time, and robustness to variations in load demand and operating conditions. The performance results validate that the developed three-layer neural network (TNN)-based MPPT gives better performance findings than Gaussian process regression (GPR), support vector regression (SVR), decision tree regression (DTR), and bagging ensemble decision tree (BEDT). In the performance evaluation phase, a vehicular FC with a rating of 1.26 kW is designed and operated within the temperature range of 320 K to 343 K for hydrogen pressure values ranging from 1 bar to 1.8 bar. For these operational conditions, the prediction accuracy value of the proposed TNN method is 99.6% while the performance values GPR, SVR, DTR, and BEDT are 99%, 98.6%, 97.2%, and 96%. In addition, system efficiency is increased by 0.98%, 1.25%, 2.51%, and 3.02% compared to GPR, SVR, DTR, and BEDT, respectively. [Display omitted] • Developing data-driven algorithms-based MPPT techniques for vehicular FCs. • Providing advantages regarding high-speed tracking capability and tracking accuracy. • Providing stable DC bus voltage operation. • Improving system performance under dynamic load changes and operating conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a new stochastic model updating method based on the homotopy surrogate model (HSM) and Bayesian sampling. As a novel intrusive surrogate model, the HSM is established by the homotopy stochastic finite element (FE) method. Then combining the advanced delayed‐rejection adaptive Metropolis–Hastings sampling technology with HSM, the structural FE model can be updated by uncertain measurement modal data. The numerical results show that the updating effectiveness of the proposed method is better than that of the Bayesian methods with the non‐intrusive surrogate models, such as stochastic response surface model and Kriging model. Compared to the Bayesian method with the intrusive second‐order perturbation model, the updating results of the proposed method are more accurate, especially when the fluctuation of the uncertain measured data is large and the stiffness of the structure significantly changes. The model updating results of a cable‐stayed bridge show that the statistic modal properties of the updated bridge model have a very good agreement with the uncertain measurement modal data. [ABSTRACT FROM AUTHOR]

With the increasing use of data-driven modeling methods, new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through the literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. The literature shows the advantages of data-driven methods: a reduction in computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies' aptitudes for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection, while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation datasets, with Gaussian process regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine. [ABSTRACT FROM AUTHOR]

The operational parameters of plant protection unmanned aerial vehicles (UAVs) significantly impact spraying effectiveness, but the underlying mechanism remains unclear. This paper conducted a full factorial experiment with varying flight speeds, heights, and nozzle flow rates to collect parameter space data. Using the Kriging surrogate model, we characterized this parameter space and subsequently optimized the average deposition rate and coefficient of variation by employing a variable crossover (mutation) probability multi-objective genetic algorithm. In the obtained Pareto front, the average sedimentation rate is no less than 46%, with a maximum of 56.08%, and the CV coefficient is no more than 13.91%, with a minimum of only 8.42%. These optimized parameters enhance both the average deposition rate and spraying uniformity compared to experimental data. By employing these optimized parameters in practical applications, a balance between the maximum average deposition rate and minimum coefficient of variation can be achieved during UAV spraying, thereby reducing pesticide usage, promoting sustainable agriculture, and mitigating instances of missed spraying and re-spraying. [ABSTRACT FROM AUTHOR]

Over the years, prediction techniques for the highly variable angular velocity of the Earth represented by Earth's rotation (UT1-UTC) and length-of-day (LOD) have been continuously improved. This is because many applications like navigation, astronomy, space exploration, climate studies, timekeeping, disaster monitoring, and geodynamic studies, all rely on predictions of these Earth rotation parameters. They provide early warning of changes in the Earth's rotation, allowing various industries and scientific fields to operate more precisely and efficiently. Thus, in our study, we focused on short-term prediction for UT1-UTC (dUT1) and LOD. Our prediction approach is to combine machine learning (ML) technique with efficient evolutionary computation (EC) algorithms to achieve reliable and improved predictions. Gaussian process regression (GPR) is used as the ML technique with genetic algorithm (GA) as the EC algorithm. GA is used for hyperparameter optimization of GPR model as selecting appropriate values for hyperparameter are essential to ensure that the prediction model can accurately capture the underlying patterns in the data. We conducted some experiments with our prediction approach to thoroughly test its capabilities. Moreover, two forecasting strategies were used to assess the performance in both hindcast and operational settings. In most of the experiments, the data used are the multi-technique combinations (C04) generated by International Earth Rotation and Reference Systems Service (IERS). In one of the experiments, we also investigated the performance of our prediction model on dUT1 and LOD from four different products obtained from IERS EOP 20 C04, DTRF20, JTRF20 and USNO. The prediction products are evaluated with real estimates of the EOP product with which the model is trained. The combined excitations of the atmosphere, oceans, hydrology, and sea level (AAM + OAM + HAM + SLAM) are used as predictors because they are highly correlated to the input data. The results depict the highest performance of 0.412 ms in dUT1 and 0.092 ms/day in LOD, on day 10 of predictions. It is worth noting that the later predictions were obtained by incorporating the uncertainty of the input data as weights in the prediction model, which was a novel approach tested in this study. [ABSTRACT FROM AUTHOR]

Spatial models are essential in the prediction of climate phenomena because they can model the complex relationships between different locations. In this study, we discuss an expanded spatial Durbin model with ordinary kriging on unobserved locations (ESDMOK) to predict rainfall patterns in Java Island. The classical spatial Durbin model needed to be expanded to obtain a parameter estimation for each location. We combined this with ordinary kriging because the data were not available in some locations. The data were taken from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) website. Since climate data are big data, we implement a big data analytics approach, namely the data analytics life cycle method. As the exogenous variables, we used air temperature, humidity, solar irradiation, wind speed, and surface pressure. The authors developed an R-Shiny web applications to implement our proposed technique. Using our proposed technique, we obtained more accurate and reliable climate data prediction, indicated by the mean absolute percentage error (MAPE), which was equal to 1.956%. The greatest effect on rainfall was given by the surface pressure variable, and the smallest was wind speed. [ABSTRACT FROM AUTHOR]

Chlorophyll monitoring is an important topic in phenotypic research. For fruit trees, chlorophyll content can reflect the real-time photosynthetic capacity, which is a great reference for nutrient status assessment. Traditional in situ estimation methods are labor- and time-consuming. Remote sensing spectral imagery has been widely applied in agricultural research. This study aims to explore a transferable model to estimate canopy SPAD across growth stages and tree species. Unmanned aerial vehicle (UAV) system was applied for multispectral images acquisition. The results showed that the univariate model yielded with Green Normalized Difference Vegetation Index (GNDVI) gave valuable prediction results, providing a simple and effective method for chlorophyll monitoring for single species. Reflection features (RF) and texture features (TF) were extracted for multivariate modeling. Gaussian Process Regression (GPR) models yielded better performance for mixed species research than other algorithm models, and the R² of the RF+TF+GPR model was approximately 0.7 in both single and mixed species. In addition, this method can also be used to predict canopy SPAD over various growth stages, especially in the third and fourth stages with R² higher than 0.6. This paper highlights the importance of using RF+TF for canopy feature expression and deep connection exploration between canopy features with GPR algorithm. This research provides a universal model for canopy SPAD inversion which can promote the growth status monitoring and management of fruit trees. [ABSTRACT FROM AUTHOR]