Set Of Weights Research Articles

An algorithm for decomposing variations of 3D model

In recent times, there has been an increasing number of people who are concerned about the virtual reality field. Parameterization of deformations of 3D models is a meaningful problem in theoretical research and application development of virtual reality. This paper proposes a technique for conditional decomposition of 3D model variations based on a given set of 3D observations of an object, along with a set of input strain weights. The proposed algorithm is conducted through an optimal iterative process with solving the non-negative least squares problem. The output of the technique is a set of base models corresponding to different types of strain. The result of the proposed technique allows the creation of a new 3D model variant of the object in a simple and visually observable way. The algorithm has been tested and proven effective on data that are 3D face models created from the Japanese Female Facial Expression (JAFFE) dataset with labeled expression weights.

A road data assets revenue allocation model based on a modified Shapley value approach considering contribution evaluation

This paper constructs a two-layer road data asset revenue allocation model based on a modified Shapley value approach. The first layer allocates revenue to three roles in the data value realization process: the original data collectors, the data processors, and the data product producers. It fully considers and appropriately adjusts the revenue allocation to each role based on data risk factors. The second layer determines the correction factors for different roles to distribute revenue among the participants within those roles. Finally, the revenue values of the participants within each role are synthesized to obtain a consolidated revenue distribution for each participant. Compared to the traditional Shapley value method, this model establishes a revenue allocation evaluation index system, uses entropy weighting and rough set theory to determine the weights, and adopts a fuzzy comprehensive evaluation and numerical analysis to assess the degree of contribution of participants. It fully accounts for differences in both the qualitative and quantitative contributions of participants, enabling a fairer and more reasonable distribution of revenues. This study provides new perspectives and methodologies for the benefit distribution mechanism in road data assets, which aid in promoting the market-based use of road data assets, and it serves as an important reference for the application of data assetization in the road transportation industry.

Operational Aviation Icing Forecast Algorithm for the Korea Meteorological Administration

Abstract In this study, we developed and evaluated the Korean Forecast Icing Potential (K-FIP), an in-flight icing forecast system for the Korea Meteorological Administration (KMA) based on the simplified forecast icing potential (SFIP) algorithm. The SFIP is an algorithm used to postprocess numerical weather prediction (NWP) model forecasts for predicting potential areas of icing based on the fuzzy logic formulations of four membership functions: temperature, relative humidity, vertical velocity, and cloud liquid water content. In this study, we optimized the original version of the SFIP for the global NWP model of the KMA through three important updates using 34 months of pilot reports for icing as follows: using total cloud condensates, reconstructing membership functions, and determining the best weight combination for input variables. The use of all cloud condensates and the reconstruction of these membership functions resulted in a significant improvement in the algorithm compared with the original. The weight combinations for the KMA’s global model were determined based on the performance scores. While several sets of weights performed equally well, this process identified the most effective weight combination for the KMA model, which is referred to as the K-FIP. The K-FIP demonstrated the ability to successfully predict icing over the Korean Peninsula using observations made by research aircraft from the National Institute of Meteorological Sciences of the KMA. Eventually, the K-FIP icing forecasts will provide better forecasts of icing potentials for safe and efficient aviation operations in South Korea. Significance Statement In-flight aircraft icing has posed a threat to safe flights for decades. With advances in computing resources and an improvement in the spatiotemporal resolutions of numerical weather prediction (NWP) models, icing algorithms have been developed using NWP model outputs associated with supercooled liquid water. This study evaluated and optimized the simplified forecast icing potential, an NWP model–based icing algorithm, for the global model of the Korean Meteorological Administration (KMA) using a long-term observational dataset to improve its prediction skills. The improvements shown in this study and the SFIP implemented in the KMA will provide more informative predictions for safe and efficient air travel.

Genetic algorithms reveal identity independent representation of emotional expressions.

People readily and automatically process facial emotion and identity, and it has been reported that these cues are processed both dependently and independently. However, this question of identity independent encoding of emotions has only been examined using posed, often exaggerated expressions of emotion, that do not account for the substantial individual differences in emotion recognition. In this study, we ask whether people's unique beliefs of how emotions should be reflected in facial expressions depend on the identity of the face. To do this, we employed a genetic algorithm where participants created facial expressions to represent different emotions. Participants generated facial expressions of anger, fear, happiness, and sadness, on two different identities. Facial features were controlled by manipulating a set of weights, allowing us to probe the exact positions of faces in high-dimensional expression space. We found that participants created facial expressions belonging to each identity in a similar space that was unique to the participant, for angry, fearful, and happy expressions, but not sad. However, using a machine learning algorithm that examined the positions of faces in expression space, we also found systematic differences between the two identities' expressions across participants. This suggests that participants' beliefs of how an emotion should be reflected in a facial expression are unique to them and identity independent, although there are also some systematic differences in the facial expressions between two identities that are common across all individuals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dynamic DEA with Common Weights: A Case Study of Iranian Airlines

The traditional data envelopment analysis (DEA) models allow each decision-making unit (DMU) to designate the most advantageous weights to maximize its relative efficiency score. Furthermore, for evaluating the performance of DMUs with consecutive processes during specified periods, dynamic data envelopment analysis (DDEA) has received considerable attention from researchers. Considering the dynamic effects, this paper develops a novel dynamic DEA approach based on the analysis of weight restrictions to achieve a common set of weights in dynamic structures. Essentially, the contribution of this research is fourfold: (1) Generating strictly positive weights. (2) Evaluating overall and periodic efficiencies simultaneously. (3) Preventing weight dissimilarity using the proposed Model. (4) Simplifying the comparison and ranking of each unit in a dynamic network nature. To elucidate the details of the proposed approach, the real data set of fourteen Iranian airlines is used to analyze their dynamic performance levels during the period 2018–2020. The results highlight that Iran Air Tour has had the best total efficiency in the years under evaluation. The findings further demonstrate the superiority and applicability of the proposed method in analyzing the dynamic structures with the carryover flows that link the consecutive periods.

Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects

Abstract Following Efron (2014), we propose an algorithm for estimating treatment effects for use by researchers employing a regression-discontinuity (RD) design. This algorithm generates a set of estimates of the treatment effect from bootstrapped samples, wherein the polynomial-selection algorithm developed by Pei, Lee, Card, and Weber (2021) is applied to each sample, the average of these RD treatment effect (RDTE) estimates is computed and serves as the overall estimate of the RDTE. Effectively, this procedure estimates a set of plausible RD estimates and weights the estimates by their likelihood of being the best estimate to form a weighted-average estimate. We discuss why this procedure may lower the estimate’s root mean squared error (RMSE). In simulation results, we show that this better performance is achieved, yielding up to a 5% reduction in RMSE relative to PLCW’s method and a 16% reduction in RMSE relative to Calonico, Cattaneo, and Titiunik’s (2014) method for bandwidth selection (with default settings).

Deformable 3D/3D CT-to-digital-tomosynthesis image registration in image-guided bronchoscopy interventions

Traditional navigational bronchoscopy procedures rely on preprocedural computed tomography (CT) and intraoperative chest radiography and cone-beam CT (CBCT) to biopsy peripheral lung lesions. This navigational approach is challenging due to the projective nature of radiography, and the high radiation dose, long imaging time, and large footprints of CBCT. Digital tomosynthesis (DTS) is considered an attractive alternative combining the advantages of radiography and CBCT. Only the depth resolution cannot match a full CBCT image due to the limited angle acquisition. To address this issue, preoperative CT is a good auxiliary in guiding bronchoscopy interventions. Nevertheless, CT-to-body divergence caused by anatomic changes and respiratory motion, hinders the effective use of CT imaging. To mitigate CT-to-body divergence, we propose a novel deformable 3D/3D CT-to-DTS registration algorithm employing a multistage, multiresolution approach and using affine and elastic B-spline transformation models with bone and lung mask images. A multiresolution strategy with a Gaussian image pyramid and a multigrid strategy within the B-spline model are applied. The normalized correlation coefficient is included in the cost function for the affine model and a multimetric weighted cost function is used for the B-spline model, with weights determined heuristically. Tested on simulated and real patient bronchoscopy data, the algorithm yields promising results. Assessed qualitatively by visual inspection and quantitatively by computing the Dice coefficient (DC) and the average symmetric surface distance (ASSD), the algorithm achieves mean DC of 0.82±0.05 and 0.74±0.05, and mean ASSD of 0.65±0.29mm and 0.93±0.43mm for simulated and real data, respectively. This algorithm lays the groundwork for CT-aided intraoperative DTS imaging in image-guided bronchoscopy interventions with future studies focusing on automated metric weight setting.

Mathematical programming models to assist the evaluation of shipbrokers

Shipbrokers play a key role in maritime industry by acting as intermediates between shipping companies and the market. They undertake various chartering, buying or selling operations. In this paper, we propose a mathematical programming approach for the evaluation and selection of shipbrokers. Specifically, the score of each ship broker is a composite measure that is derived by aggregating a set of performance criteria, e.g., reputation, etc. The developed mathematical programming models enable the aggregation and weighting of the criteria. We employ three optimization models to explore the effect of different weighting schemes on the scores and ranking of the shipbrokers. The models that provide a common set of weights for all the shipbrokers establish the appropriate ground for comparisons among them. Also, our models facilitate the incorporation of user priorities over the criteria in the form of weight restrictions. The proposed approach is illustrated by assessing seven shipbroker offers for selling a dry-bulk ship using four criteria, namely revenue, brokerage fee, brokerage time and terms & conditions.

KarstNSim: A graph-based method for 3D geologically-driven simulation of karst networks

In karst aquifers, groundwater flow is highly influenced by the interconnected underground cavities and conduits that form the karst network. Modeling karst flows requires the use of spatially distributed approaches accounting for these networks. Their exploration is, however, often complex, and mapping them using indirect methods such as geophysical ones has proven challenging. To overcome these limitations, stochastically simulating discrete karst networks should account for the uncertainties on conduit position and geometry. Only a few existing methods can reproduce realistic and diverse karst morphologies. In this work, we propose a new algorithm, KarstNSim, for simulating discrete karst networks, that incorporates field data to generate a range of possible karst network geometries. It relies on the computation of the shortest path between the inlets and outlets of the network with the use of an anisotropic cost function defined on an n-nearest neighbor graph conformal to geological and structural heterogeneities. This cost function represents the physico-chemical processes that govern speleogenesis – such as erosion and chemical weathering – providing simplified control over the geometry of the generated networks. Our approach reproduces the vadose-phreatic partition visible in the karst networks, by generating sub-vertical conduits in the unsaturated zone and sub-horizontal ones in the saturated part. It encompasses geological parameters such as inception surfaces, fractures, permeability, and solubility of layers, along with considering the hydrological context of recharge by assigning relative weights to the inlets. We simulate various synthetic models to demonstrate the influence of different input parameters on the spatial organization of the past and present karst flows. We also apply KarstNSim to a real case study – the Ribeaucourt system (Meuse, France) – and use available data to generate realistic karst networks. The lack of knowledge on the Ribeaucourt network geometry is alleviated by the use of multiple scenarios corresponding to different sets of weights in the cost function, resulting in a variety of network morphologies which represent the uncertainty on the real geometry. The geometrical and topological metrics computed on the simulated networks generally overlap with those of real ones, suggesting the reliability of the method.

Optimization for Calibration of Survey Weights under a Large Number of Conflicting Constraints

In the analysis of survey data, sampling weights are needed for consistent estimation of the population; however, weights are typically modified through a process termed “calibration” to increase their efficiency and stability by ensuring weighted sums of auxiliary variables match a collection of controls. It is often the case that no single set of weights can be found that simultaneously incorporates all of these controls. Together they induce a large number of constraints and restrictions that don’t produce a feasible solution space. We present an optimization framework and an accompanying fast computational methodology to address this issue of constraint achievement or selection within a restricted space that will produce a stabilized set of calibrated weights. Our approach comes closest to the simultaneous achievement of a large number of conflicting constraints, while providing diagnostics about which constraints may not be exactly met. Our motivating example is the post-stratification for the National Survey on Drug Use and Health. We also make connections to covariate balancing approaches for observational studies. Computations were performed in R and code is provided in the supplementary material.

Research on optimization method of railway construction scheme based on multidimensional combination weighting and improved grey theory

The optimization of railway construction schemes is a complexity system engineering task with multiple dimensions, diverse conditional constraints, and multifaceted objective assessments. The decision-making and scheme evaluation entail subjectivity, randomness, and fuzziness. To address the comprehensive optimization challenge in construction schemes effectively and efficiently, we investigate an optimization method for railway construction schemes. This method is based on multi-dimensional combination weighting and improved grey theory. After analyzing the primary influencing factors, we established a railway construction plan optimization index system comprising 4 dimensions and 18 factors. The weight combination coefficient is determined using the pros and cons solution distance method, and the optimal weight set for the index is determined through the multi-dimensional combination weighting approach. Utilizing the method of superior and inferior solution distance coupled with grey theory, we ascertain the order of advantages and disadvantages for each construction scheme, subsequently achieving construction scheme optimization. To illustrate this, we employ the optimization process for a high-speed railway section in Guangxi as an exemplar. The verification results indicate that the gray relative closeness values for schemes A, B, and C are 0.7089, 0.4813, and 0.4463, respectively. Scheme A has the highest gray relative closeness value, thus making it the optimal route scheme. The optimal results obtained through this method align with the outcomes of expert validation and existing research, thereby validating the effectiveness and practicality of the model. By employing a multidimensional combination weighting method, the deficiencies of traditional indicator weight calculations are mitigated, resulting in indicator weights that are more reflective of the actual circumstances. At the same time, the application of improvements in the grey theory comprehensive evaluation method enables the integration and computation of indicator data for each construction plan. Through the intuitive representation of grey relative closeness, the advantages and disadvantages of each plan are effectively characterized. This enhances the scientific rigor and applicability of the railway construction plan optimization process. The research findings can serve as a reference for similar railway construction scheme selection problems in the future.

Signal Quantification of Intravenous Contrast Agents Enhancement from Biphase Liver CT Scan Procedures

In multi-detector computed tomography (MDCT) abdominal and pelvic CT treatments with intravenous (IV) contrast media (CM), automated bolus monitoring with a fixed contrast enhancement delay was examined. Statistics assess all contrast enhancement variables, including patient data like body weight, cardiac output, and contrast injection settings. This study comprised 100 retrospective and 43 prospective patients. In the first group, the Hounsfield unit (HU) was measured before, 30 seconds, and 70 seconds after CM. The second group measured age, weight, heart rate, systolic and diastolic blood pressure, and creatinine. The radiographer computed CM time based on HU values around 120. The differences in HU levels across groups were used to create an equation for imaging time prediction utilizing auto-mated bolus monitoring. The Bolus Time Equation’s predictors included patient weight, heart rate, creatinine level, and systolic blood pressure, with 34.9% dependency and 59.1% influence on each variable. The equation is trustworthy since the ANOVA test indicated p = 0.002. The computation and study gave the same Bolus Time value with a p-value of (0.992 > 0.05). The first and second groups exhibited very different HU rates (p-value 0.00). The research found that fixed-time improved more than bolus monitoring, which performed better.

Bi-Level Multiobjective Evolutionary Learning: A Case Study on Multitask Graph Neural Topology Search

The construction of machine learning models involves many bi-level multi-objective optimization problems (BL-MOPs), where upper level (UL) candidate solutions must be evaluated via training weights of a model in the lower level (LL). Due to the Pareto optimality of sub-problems and the complex dependency across UL solutions and LL weights, an UL solution is feasible if and only if the LL weight is Pareto optimal. It is computationally expensive to determine which LL Pareto weight in the LL Pareto weight set is the most appropriate for each UL solution. This paper proposes a bi-level multi-objective learning framework (BLMOL), coupling the above decision-making process with the optimization process of the UL-MOP by introducing LL preference r. Specifically, the UL variable and r are simultaneously searched to minimize multiple UL objectives by evolutionary multi-objective algorithms. The LL weight with respect to r is trained to minimize multiple LL objectives via gradient-based preference multi-objective algorithms. In addition, the preference surrogate model is constructed to replace the expensive evaluation process of the UL-MOP. We consider a novel case study on multi-task graph neural topology search. It aims to find a set of Pareto topologies and their Pareto weights, representing different trade-offs across tasks at UL and LL, respectively. The found graph neural network is employed to solve multiple tasks simultaneously, including graph classification, node classification, and link prediction. Experimental results demonstrate that BLMOL can outperform some state-of-the-art algorithms and generate well-representative UL solutions and LL weights.

DeepLS: Local Search for Network Optimization Based on Lightweight Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is being investigated as a competitive alternative to traditional techniques for solving network optimization problems. A promising research direction lies in enhancing traditional optimization algorithms by offloading low-level decisions to a DRL agent. In this study, we consider how to effectively employ DRL to improve the performance of Local Search algorithms, i.e., algorithms that, starting from a candidate solution, explore the solution space by iteratively applying local changes (i.e., moves), yielding the best solution found in the process. We propose a Local Search algorithm based on lightweight Deep Reinforcement Learning (DeepLS) that, given a neighborhood, queries a DRL agent for choosing a move, with the goal of achieving the best objective value in the long term. Our DRL agent, based on permutation-equivariant neural networks, is composed by less than a hundred parameters, requiring only up to ten minutes of training and can evaluate problem instances of arbitrary size, generalizing to networks and traffic distributions unseen during training. We evaluate DeepLS on two illustrative NP-Hard network routing problems, namely OSPF Weight Setting and Routing and Wavelength Assignment, training on a single small network only and evaluating on instances 2x-10x larger than training. Experimental results show that DeepLS outperforms existing DRL-based approaches from literature and attains competitive results with state-of-the-art metaheuristics, with computing times up to 8x smaller than the strongest algorithmic baselines.

Optimasi Parameter Artificial Neural Network (ANN) Menggunakan Particle Swarm Optimization (PSO) Untuk Pengkategorian Nasabah Bank

Artificial Neural Network (ANN), also known as Jaringan Saraf Tiruan, is one of the methods commonly used for pattern recognition, classification, forecasting, and regression, depending on the problem or data used. While the results obtained are generally good, there are often issues with determining the initial parameters as the initial weights, which can lead to non-convergence of results. This is why a method is needed to optimize the ANN parameters to achieve better outcomes. Particle Swarm Optimization (PSO) was chosen as the method to optimize the ANN parameters (PSO-ANN). The best parameter values for PSO were predefined, with w (inertia weight) set to 0.8 and c1 and c2 (acceleration coefficients) set to 1.5. Subsequently, PSO-ANN was trained using a bank customer dataset to determine the categories of customers with credit problems or not. The results were compared with using ANN without parameter optimization. The obtained results showed an Accuracy rate of 82.6%, Precision of 91.1%, and Recall of 37.1%. This represents an improvement compared to the results of ANN without parameter optimization, which had an Accuracy rate of 80.1%, Precision of 89.5%, and Recall of 32.4%.

A novel model for merger analysis and target setting: A CSW-Inverse DEA approach

The inverse data envelopment analysis (DEA) is an advanced complementary method for efficiency analysis using the classical DEA approach. One of the inverse DEA (InvDEA) method applications is the mergers and acquisitions problem. It can be used for analyzing any under evaluation mergers and acquisitions. The current article is the first attempt to propose a novel inverse structure for the DEA model using the multiplier forms. This eventuates the possibility of incorporating decision maker preferences within the merger analysis.Moreover, compared with the existing models in the literature, the proposed novel models are capable of analyzing multiple merger scenarios simultaneously in a single method based on the common set of weights (CSW) rather than a series of models for studying multiple scenarios of mergers and acquisitions. Practically, this property enables decision-makers to consider and analyze multiple mergers and find possible potentials at the same time. The applicability of the proposed model is investigated by using a real-world dataset in banking.

Pseudo-metric modelling of distribution network state estimation based on CNN-BiLSTM network and customized HGGA algorithm

To address the insufficient configuration of real-time measurement devices in distribution networks, a state estimation method for distribution networks is proposed. First, both historical data and real-time data are used as the input of the convolutional neural networks-bi-directional long short-term memory (CNN-BiLSTM) network to obtain the pseudo measurements of the branch power and load node injected power with high accuracy at the current moment. Second, the pseudo measurement set of the input state estimation is determined using the customized hybrid greedy genetic algorithm (HGGA) algorithm. Finally, the real-time measurement, pseudo measurement, virtual measurement, and weight set are input into the state estimation program to complete the real-time state estimation of the distribution network based on the weighted least squares method. Additionally, the proposed method is applied to the data fusion of different measurement systems. Theoretical analysis and example verification confirm that the proposed method can effectively improve the accuracy of pseudo measurement modelling and state estimation results.

On-line posture measurement for automatic positioning of foldable-wing with priority-based multi-constraints

The digital assembly system is widely used in the automatic positioning of large aircraft components. Accurate posture measurement of the components remains challenging for high-precision assembly of the components with various engineering constraints. Existing methods often require repeated measurements of key features and are susceptible to the pronounced coupled effect, where various constraints mutually influence the solution of pose parameters. To enhance the efficiency and precision of posture measurement, this study proposes an on-line measurement method for the Foldable-wing Digital Assembly System. The key features and several reference points are measured once by the laser tracker. According to the assembly task sequence, a novel posture evaluation model with priority-based multi-constraints is established to calibrate these reference points which will be utilized as the new datum of posture measurement instead of the traditional key features. This model separately satisfies the various engineering constraints, such as coaxiality, gap, and basic positioning accuracy, in order of priority to avoid the coupling effect between different types of constraints. Consequently, features with higher priority can achieve higher accuracy without weight setting. Next, the reference points are automatically measured to calculate the posture parameters. Considering the uncertainty characteristics of the measuring instrument, the registration algorithm based on weighted total least squares adjustment is applied to reduce measurement uncertainty. Monte Carlo simulation results demonstrate the proposed method's ability to enhance positioning accuracy and reduce posture uncertainty, outperforming conventional methods. Finally, the practical application was successfully implemented, and all key features remained within tolerance.

A Multi-Objective Perspective to Satellite Design and Reliability Optimization

Development of a communication satellite project is highly complicated and expensive which costs a few hundred million dollars depending on the mission in space. Once a satellite is launched into orbit, it has to operate in harsh environmental conditions including radiation, solar activity, meteorites, and extreme weather patterns. Since there is no possibility of physical maintenance intervention in space, reliability is a critical attribute for all space and satellite projects. Therefore, the redundancy philosophy and reliability measures are taken into account in the design phase of a satellite to prevent the loss of functionality in case of a failure in orbit. This study aims to optimize the payload design of a communication satellite by considering the system’s reliability, power consumption and cost simultaneously. Since these objectives are conflicting in their nature, a multi-objective optimization approach is proposed. We offer a systematic approach to the satellite design by determining the best redundancy strategy considering contradictory objectives and onboard constraints in the multibillion-dollar satellite industry. The proposed approach promotes trade-offs and sensitivity analyses between cost, power consumption and system reliability in the early design phase of satellites using Compromise Programming. By using different sets of weights for the objectives in our model, it is possible to address different types of satellites depending on their mission and priorities. Because of the NP-Hard characteristics of the reliability optimization problem and the nonlinear equation in the proposed model, the Simulated Annealing algorithm is utilized to solve the problem. As a case analysis, the implementation is carried out on the design of a communication satellite system with active hot-standby and warm-standby onboard redundancy schemes. Results reveal that huge savings in million dollars can be attained as a result of approximately 5% reduction in reliability.

The Dewatering Performance of a Compact Screw Press Manure Separator for Non-Typical Substrates

The dewatering of substrates is one of the key tasks in the handling of material flows in a circular economy. Besides belt filters, sedimenters, and decanters, screw presses are frequently used to reduce the water content of substrates. However, screw presses available on the market are usually designed for high throughputs (>5 m3/h). The dewatering of smaller feed rates (<1 m3/h), however, requires more compact solutions. For this reason, a compact screw press (short screw length), which is normally used in agriculture for cow manure dewatering, was investigated in this work, as there exist no publications on this type of compact screw press. A mobile test setup was developed and loaded with different material such as digested sludge from a wastewater treatment plant, commercially available potting soil, wood shavings, and biological waste. The dry matter content of the feed material, the feed material itself, the screw rotational speed, and the weight setting (pressure) at the back plate were varied, and the effects on the dewatering result were recorded. A significant influence on the dewatering result was found for the weight setting, the type of substrate (particle size), and the dry substance content of the feed material. The tests confirmed that the compact screw press separator is suitable for the dewatering of wood shavings and biological waste (with an increase in dry matter percentage of up to 7.7%). The direct dewatering of potting soil and digested sludge is not possible and can only be implemented by using structural support material. This study fills the gap between published work on large screw presses and potentially new application fields of compact screw press manure separators, which, until now, have only been used in agriculture.