Precise Calculation Research Articles

Design and Fabrication of the Automatic Multipurpose Agriculture Robot (AMAR)

The rapid development of robotics and automation has significantly benefited various industries, including agriculture. This project proposes the creation of an Automatic Multipurpose Agriculture Robot (AMAR) to revolutionize farming practices. AMAR will automate diverse agricultural tasks, minimize physical labour, and optimize resource use. The automatic multipurpose agricultural seed sowing robot, a state-of-the-art autonomous machine, is designed to revolutionize seed planting. It combines technologies like computer vision and precision mechanics to ensure efficient and precise seed sowing across various crops and terrains. The robot analyses optimal seed placement, ensuring even distribution. Energy-efficient and eco-friendly, it reduces labour costs, increases productivity, and enhances crop yields, fostering sustainable agricultural practices and improving profitability for farmers.The rapid development of robotics and automation has significantly benefited various industries, including agriculture. This project proposes the creation of an Automatic Multipurpose Agriculture Robot (AMAR) to revolutionize farming practices. AMAR will automate diverse agricultural tasks, minimize physical labour, and optimize resource use. The automatic multipurpose agricultural seed sowing robot, a state-of-the-art autonomous machine, is designed to revolutionize seed planting. It combines technologies like computer vision and precision mechanics to ensure efficient and precise seed sowing across various crops and terrains. The robot analyses optimal seed placement, ensuring even distribution. Energy-efficient and eco-friendly, it reduces labour costs, increases productivity, and enhances crop yields, fostering sustainable agricultural practices and improving profitability for farmers.

Modeling of Lithium-Ion Batteries for Electric Transportation: A Comprehensive Review of Electrical Models and Parameter Dependencies

The power and transportation sectors contribute to more than 66% of global carbon emissions. Decarbonizing these sectors is critical for achieving a zero-carbon economy by mid-century and mitigating the most severe impacts of climate change. Battery packs, which enable energy storage in electric vehicles, are a key component of electrified transport systems. The production of these batteries has significantly increased in recent years to meet rising demand, and this trend is expected to continue. However, current traction batteries exhibit lower energy density compared to fossil fuels. As a result, accurate battery models that balance computational complexity and precision are essential for designing high-performance energy storage systems. This paper provides a comprehensive review of the most used electrical models for lithium-ion batteries in traction applications, as reported in the technical literature. By exploring the strengths and limitations of different modeling approaches, this paper aims to offer valuable insights into their practical applicability for the electrification of transportation systems. Additionally, this paper discusses the primary methods employed to derive the values of the electrical components within these models. Finally, it examines the key parameters—such as temperature, state of charge, and aging—that significantly influence the component values. Ultimately, it guides researchers and practitioners in selecting the most suitable modeling approach for their specific needs.

Development of Software for Hydrometallurgical Calculation of Metal Extraction

Hydrometallurgy plays a critical role in the metallurgical industry by providing an efficient method for extracting metals from ores and secondary materials using aqueous solutions. This approach is particularly advantageous for processing low-grade and complex ores, as well as secondary resources that cannot be effectively processed by traditional pyrometallurgical methods. The objective of this study is to develop specialized software to automate and optimize the calculations necessary for metal extraction in hydrometallurgical processes. The software integrates a complete computational framework for automating the various stages of the hydrometallurgical process, including ore composition initialization, total element mass calculations, and the determination of metal concentrations in both metal products (matte) and by-products (slag). The methodology involves the design and implementation of a web-based application using Django for the user interface and MySQL for robust data storage. The computational module, written in Python, automates the mathematical operations required to simulate complex chemical reactions and metal extraction processes. This module supports real-time processing and ensures accurate calculations for each stage of metal extraction, including leaching, extraction, re-extraction, sorption, and electrolysis. The software also features detailed tables and dynamic graphs that allow users to analyze the distribution of valuable metals and assess the influence of key operational parameters on extraction efficiency. The results show that the developed software successfully manages large datasets, enhances the precision of hydrometallurgical calculations, and minimizes the risk of human error. The implementation of the software leads to significant improvements in the economic efficiency of production by optimizing metal recovery rates and reducing operational costs. Additionally, it supports comprehensive process control by providing actionable insights into each stage of extraction, thus improving the quality of the final metal product. Overall, the software represents a significant technological advancement in the field, offering a scalable solution for industries aiming to streamline hydrometallurgical processes.

DeepAge: Harnessing Deep Neural Network for Epigenetic Age Estimation From DNA Methylation Data of Human Blood Samples

Accurate prediction of biological age from DNA methylation data is a critical endeavor in understanding the molecular mechanisms of aging and developing age-related disease interventions. Traditional epigenetic clocks rely on linear regression or basic machine learning models, which often fail to capture the complex, non-linear interactions within methylation data. This study introduces DeepAge, a novel deep learning framework utilizing Temporal Convolutional Networks (TCNs) to enhance the prediction of biological age from DNA methylation profiles using selected CpGs by a Dual-Correlation based apparoach. DeepAge leverages a sequence-based approach with dilated convolutions to effectively capture long-range dependencies between CpG sites, addressing the limitations of prior models by incorporating advanced network architectures including residual connections and dropout regularization. The dual correlation feature selection enhances our model's predictive capabilities by identifying the most age-relevant CpG sites. Our model outperforms existing epigenetic clocks across multiple datasets, offering significant improvements in accuracy and providing deeper insights into the epigenetic determinants of aging. The proposed method not only sets a new standard in age estimation but also highlights the potential of deep learning in biologically relevant feature extraction and interpretation, contributing to the broader field of computational biology and precision medicine.

Re-evaluation of F-FDG absorbed and effective dose in adult and pediatric phantoms using DoseCalcs Monte Carlo platform: a validation study.

Positron emission tomography (PET) using F-FDG is a well-known modality for the diagnosis of various diseases in patients of different ages, sexes, and states of health, which implies that internal radiation dosimetry is highly desired for different phantom anatomies. In this study, we validate "DoseCalcs," a new Monte Carlo platform that combines personalized internal dosimetry calculations with Monte Carlo simulations. To achieve that, we used the specific absorbed fraction (SAF) calculated by DoseCalcs and those from ICRP publication 133 to estimate the absorbed dose per injected activity (AD/IA) and effective dose per injected activity (ED/IA) for F-FDG. The investigation focused on various voxelized phantoms representing different age groups, including adult male and female, and pediatric phantoms of various ages, from newborn to 15 years old. Using the DoseCalcs Monte Carlo platform, we have simulated the emission of F-FDG positrons based on the energy spectrum provided in ICRP publication 107. The results demonstrated the impact of anatomical differences and different organ/tissue compositions on radiation absorption, with significant variations in the AD/IA across different phantoms. Interestingly, organs/tissues near the emission source showed higher AD/IA, highlighting the anatomical dependence on the phantom. When our results were compared to established reference data, especially from ICRP128, most organs/tissues had good agreement. Still, some cases have shown differences. This shows how important it is to use accurate radionuclide data and biokinetic modeling in internal dosimetry calculations. Furthermore, we compared AD/IA and ED/IA values calculated in newborns by DoseCalcs with those derived from alternative codes, MCNP and EGSnrc. While the results generally exhibited consistency, subtle variations underscored the influence of biokinetics modeling choices and computational methodologies. Overall, this research contributes valuable insights into the precision of internal dosimetry calculations using "DoseCalcs-Gui" by providing one platform for Monte Carlo simulation and personalized internal dosimetry in nuclear medicine. The DoseCalcs platform is free for research and available for download at www.github.com/TarikEl/DoseCalcs-Gui .

Integrated photonic modular arithmetic processor

Integrated photonic computing has emerged as a promising approach to overcome the limitations of electronic processors in the post-Moore era. However, present integrated photonic computing systems face challenges in achieving high-precision calculations, consequently limiting their potential applications, and their heavy reliance on analog-to-digital (AD) and digital-to-analog (DA) conversion interfaces undermines their performance. Here we propose an innovative photonic computing architecture featuring scalable calculation precision and, to our knowledge, a novel photonic conversion interface. By leveraging the residue number system (RNS) theory, the high-precision calculation is decomposed into multiple low-precision modular arithmetic operations executed through optical phase manipulation. Those operations directly interact with the digital system via our proposed optical digital-to-phase converter (ODPC) and phase-to-digital converter (OPDC). Through experimental demonstrations, we showcase a calculation precision of 9 bits and verify the feasibility of the ODPC/OPDC photonic interface. This approach paves the path towards liberating photonic computing from the constraints imposed by limited precision and AD/DA converters.

Efficient aerodynamic shape optimization using transfer learning based multi-fidelity deep neural network

Computational efficiency and precision pose a classic contradiction in aerodynamic shape optimization. To address this challenge, this study introduces an effective optimization framework based on multi-fidelity fully connected neural network (MFFCN). The framework utilizes transfer learning (TL) to train a multi-fidelity surrogate model that establishes direct mappings between geometric configuration parameters and aerodynamic performance by adaptively capturing linear or nonlinear relationships concealed between high-fidelity (HF) and low-fidelity (LF) information. The HF and LF data are derived from fine and coarse grids, respectively, evaluated using the same computational fluid dynamics (CFD) model. The MFFCN-TL framework is applied to optimize the National Advisory Committee for Aeronautics 0012 (NACA0012) airfoil (12 design variables) and the Office National d'Études et de Recherches Aérospatiales M6 (ONERA M6) wing (50 design variables). Simulation results demonstrate that the NACA0012 airfoil achieves a 69.47% enhancement in lift–drag ratio in 1.069 s, compared to a 12.76% gain over 24.8 h in single-fidelity CFD-based optimization. The ONERA M6 wing achieves a 24.66% reduction in drag coefficient in 694 ms compared to 18.37% over 237.3 h in the CFD model. Statistical results show that the MFFCN-TL framework can reduce optimization cost by more than 90% compared to the single-fidelity CFD-based model. These findings suggest that the MFFCN-TL framework significantly enhances optimization efficiency and provides superior feasible solutions over single-fidelity methods.

Benchmark Simulation of Laminar Reactive Micromixing Using Lattice Boltzmann Methods

Micromixers are chemical processing devices with complex flow patterns applied for both mixing and reaction of chemical species. In current research, laminar reacting multicomponent flows are considered. Despite the laminar streaming regime (e.g., Re = 186), there exist secondary flow microstructures. For this setup, accurate predictions of those structures are possible with a large-eddy simulation on a fine mesh resolving till the Batchelor microscales. Utilizing the open-source lattice Boltzmann method (LBM) framework, OpenLB, a benchmark simulation of the reacting micromixer, is re-established with new, more precise computation results. In this context, a Schmidt-number-based stabilization method for LBM-discretized reactive advection–diffusion equations by laminar secondary flow structures is used. A convergence study is performed, which is also a novelty. All computations have been performed on the high-performance computing cluster HoreKa using up to 160 NVIDIA A100 graphics processing units.

Current-Based Analysis and Validation of a Wheel–Soil Interaction Model for the Trafficability of a Planetary Rover

The assessment of trafficability for planetary rovers in relation to non-geometric hazards is a crucial issue in deep space exploration. This study relies on terramechanics theory and incorporates actual data from Mars soil and rover parameters to develop a model that accurately represents the interaction between the rover’s wheels and Martian soil. Through numerical simulations, this model specifically investigates the relationship between the current of the rover’s wheel drive motor and factors such as slip ratio, soil pressure parameters, and soil shear parameters. Terrestrial experiments are also conducted to verify the precision of certain numerical calculations. The proposed wheel–soil interaction model, based on wheel motor current, provides a foundation for assessing non-geometric trafficability and the inversion of planetary soil parameters.

Dynamic Analysis of a Vehicle–Bridge System Under Excitation of Random Road Irregularities

This paper presents a comprehensive study of the dynamic response analysis of vehicle–bridge coupled systems, with detailed simulation methods for the vehicles, bridges, and wheel–road coupling relationships. The simulation of the entire vehicle–bridge coupling system is carried out using the open-source finite element analysis platform OpenSees. A novel three-dimensional wheel–road coupling element is introduced to model the interactions between the wheel and road nodes. This element facilitates precise computation of the dynamic responses within the vehicle–bridge coupled system, including both vehicle and bridge behaviors, along with the interaction forces between the wheels and the bridge surface. The coupling element consists of a wheel node and all potential road nodes on the bridge surface that the wheel may traverse. This configuration preserves the finite element model of the entire vehicle–bridge coupled system throughout the vehicle’s movement, thereby improving the efficiency of numerical simulations of vehicle–road interactions. The study accounts for the impact of random road irregularities on the dynamic responses of both the vehicle and the bridge. These irregularities are treated as input parameters for the wheel–road coupling element rather than being accounted for through the wheel–road interaction constraint equations, thereby improving the convenience of simulating random road irregularities.

Systematic Uncertainties from Gribov Copies in Lattice Calculation of Parton Distributions in the Coulomb gauge

Abstract Recently, it has been proposed to compute parton distributions from boosted correlators fixed in the Coulomb gauge within the framework of Large-Momentum Effective Theory. This method does not involve Wilson lines and could greatly improve the efficiency and precision of lattice QCD calculations. However, there are concerns about whether the systematic uncertainties from Gribov copies, which correspond to the ambiguity in lattice gauge-fixing, are under control. This work gives an assessment of the Gribov copies’ effect in the Coulomb-gauge-fixed quark correlators. We utilize different strategies for the Coulomb-gauge fixing, selecting two different groups of Gribov copies based on the lattice gauge configurations. We test the difference in the resulted spatial quark correlators in the vacuum and a pion state. Our findings indicate that the statistical errors of the matrix elements from both Gribov copies, regardless of the correlation range, decrease proportionally to the square root of the number of gauge configurations. The difference between the strategies does not show statistical significance compared to the gauge noise. This demonstrates that the effect of the Gribov copies can be neglected in the practical lattice calculation of the quark parton distributions.

Advanced Modeling of Hydrogen Turbines Using Generalized Conformable Calculus

This article addresses critical challenges in the transition to clean energy sources by highlighting the importance of advanced mathematical modeling and computational techniques in turbine design and operation. Specifically, we extend and generalize the work of Camporeale to advance the modeling of hydrogen turbine systems. By utilizing conformable calculus, we develop dynamic equations that analyze key aspects of turbine performance, including temperature variations in turbine blades, angular velocities of rotating shafts, and mass--energy balances within the plenum and combustion chamber. Furthermore, we incorporate Kirchhoff's equation in its generalized conformable integral form, enhancing the precision of energy balance calculations and improving the representation of heat transfer processes in the combustion chamber. This methodology introduces novel perspectives in hydrogen turbine research, contributing to the advancement of sustainable and efficient technologies. Our comprehensive approach aims to provide more accurate and efficient predictions of turbine behavior, thereby impacting the design and optimization of hydrogen-based clean energy systems.

Modified Easley formula for elastic critical global shear buckling stress of corrugated steel webs considering real boundary conditions

The real juncture between corrugated steel webs (CSWs) and flanges follows a multi-segmented line, distinct from that of flat steel webs. Classic methods may yield significant deviations in predicting the elastic global shear buckling capacity of CSWs of various scales due to their failure to consider real boundary constraints. Therefore, a universally applicable formula for calculating the elastic critical global shear buckling stress of CSWs, which accounts for real boundary conditions, is proposed. This formula is pertinent to both large-scale engineering CSWs and small-scale testing CSWs. This study commenced with a comprehensive reassessment of the elastic global shear buckling calculation method. Subsequently, the influence of geometric parameter ratios on the elastic critical global buckling stress was examined. The primary parameter was identified and employed to improve the global buckling coefficient. The proposed calculation method was validated using different corrugation configurations, including 1000-type, 1200-type, 1600-type, 1800-type, and 2000-type CSWs, as well as other CSWs used in experimental settings. These results were compared with those obtained from other reference methods. Findings indicate that the accuracy of the classic theoretical method is affected by variations in both boundary conditions and geometric dimensions due to the constraint effect of real boundary conditions. Under the real boundary conditions, the elastic critical global shear buckling stress of CSWs with simply supported boundary conditions is close to that of CSWs with consolidated boundary conditions. The ratio of web height to corrugation depth primarily affects the elastic global shear buckling capacity, which decreases as the ratio increases. The Easley formula can be modified based on the web height to corrugation depth ratio. Comparisons of numerous numerical and theoretical results reveal that the proposed calculation method exhibits commendable computational precision. In comparison to alternative formulas, the proposed method demonstrates enhanced consistency for calculating CSWs with varying geometric dimensions and boundary conditions, thereby demonstrating its favorable applicability. These conclusions provide valuable reference for the shear design of CSWs.

Comparison of intraocular lens power formulas for negative-diopter intraocular lens implantation for high myopia.

To compare the accuracy of intraocular lens (IOL) power calculation formulas for myopic eyes requiring negative diopter powered IOLs. Retrospective case series. K… hospital and Y… Hospital, …, …. Sixty-one eyes that underwent phacoemulsification with implantation of a negative power IOL were investigated. The trueness, precision and accuracy of IOL power calculation were assessed for the Barrett Universal II (BUII), EVO 2.0, Haigis, Hoffer QST, Holladay 1 and SRK/T formulas using the Eyetemis online tool. The analysis was performed using 1) the ULIB IOL constants and 2) after constant optimization. With ULIB constants, the Haigis, Holladay 1 and SRK/T resulted in a hyperopic mean prediction error (PE) >1.00 diopter (D), which was significantly different from zero (adjusted p <0.05). The mean PE of the remaining formulas was closer to zero. The absolute PE was significantly higher with the Holladay 1 and SRK/T (adjusted p <0.05) with respect to the remaining formulas. After constant optimization, the outcomes of traditional formulas improved and no statistically significant differences were found among any of the formulas in terms of trueness, precision and accuracy. The percentage of eyes with an absolute PE within 0.50 D was low (<50%) even after constant optimization. With ULIB constants, the BUII, EVO 2.0 and Hoffer QST were more accurate than traditional formulas in eyes with negative-diopter IOLs. The results of IOL power calculation in these eyes remain poor even after constant optimization.

Sequential bias-corrected weighted least squares solution of mixed additive and multiplicative error models

In the era of big data, the number of observations in adjustment calculations may reach tens or even hundreds of thousands. When dealing with these large dataset problems, many matrix operations are often required. At this time, the dimensions of the matrix will be large, which will generate a great computational burden. At present, no research results have been published on the computational efficiency of bias-corrected weighted least squares (bcWLS) for mixed additive and multiplicative error models (MAMEM). Sequential adjustment (SEA) groups the observations for calculation and can provide the same computational precision while greatly improving computational efficiency. This paper applies the idea of SEA to the calculation of bcWLS and proposes an iterative solution for sequential bcWLS (SEbcWLS). Using three simulation experiments to verify the effectiveness of our method, it was found that when the number of observations is 10000, the effect is better when the number of groups does not exceed 100, achieving the same precision as the original method while having high computational efficiency. The calculation results of line fitting and plane fitting are not affected by the number of grouping groups. For DEM (Digital elevation model) experiments with strong nonlinearity, when the number of grouping groups is too large, the effect is not very good, but the calculation efficiency is also higher than the original method, and the difference in calculation results is not significant.

Precision computation of one-dimensional quadratures

The calculation of quadratures arises in many physical and technical applications. In this paper, the best quadrature formulas are selected and their quantitative comparison is carried out on a number of representative examples.

An improved radial basis reproducing kernel particle method for geometrically nonlinear problem analysis of SMAs

In this paper, the radial basis function (RBF) without shaped parameter is utilized in the radial basis reproducing kernel particle method (RRKPM), and an improved radial basis reproducing kernel particle method (IRRKPM) is proposed. Compared with traditional RKPM, the IRRKPM effectively reduces the impact of different kernel functions on calculation precision, and is further employed to examine geometrically nonlinear problems associated with shape memory alloys (SMAs). The displacement boundary condition is enforced via the penalty function method, while the Galerkin integration method in its weak form, along with the total Lagrangian (TL) approach, is utilized to derive the geometrically nonlinear equations for SMAs within the IRRKPM framework. The equilibrium equations are then solved using the Newton Raphson (N-R) iterative method. The impact of the different penalty factor and the radius control parameter of influence domain on errors is analyzed, the computational precision of the IRRKPM is compared with the RRKPM, and the computational stability is evaluated. Finally, the suitability of the IRRKPM for the analysis of geometrically nonlinearity problems in SMAs are confirmed through specific numerical examples.

Exploring comparative heterogeneity management for precise water saturation assessment in carbonate formations: Dean-Stark measurements and beyond

Accurate determination of water saturation is a critical aspect of reservoir characterization and development potential of a hydrocarbon field. The inherent heterogeneity in carbonate reservoirs significantly influences the Archie equation coefficients and, consequently, water saturation calculations. In this study, 160 direct core sample water saturation measurements using the Dean-Stark method have been used. The primary goal was to identify the most suitable reservoir heterogeneity management approach for water saturation calculation. The research focuses on the Dalan and Kangan formations (equivalent to Khuff Formation), known as some of the most hydrocarbon-rich (gas) reservoirs globally, located in the western part of the Persian Gulf. To manage reservoir heterogeneity, we employed various techniques, including Winland R35, flow zone indicator, Lucia, pore types, and electrofacies, utilizing porosity and permeability core data, thin section studies, and well-logging data to classify the studied intervals into more homogeneous categories. Subsequently, we measured Archie parameters and, for the first time, water saturations derived from Dean-Stark measurements were compared with Archie saturations from various heterogeneity management methods. Additionally, the influence of geological and petrophysical parameters on the accuracy of water saturation calculations in different rock types is discussed. Our results highlighted the significance of geometrical attributes of pore types, pore throat radius, and permeability as key factors affecting the precision of water saturation calculations. Moreover, our investigation revealed that Lucia's rock typing methodology exerted a substantial influence on the control of permeability and the distribution of water saturation within the reservoir. Our analysis revealed that the electrofacies method provided the most precise estimates for water saturation, with a mean difference of 0.07 (v/v) units compared to the Dean-Stark method. Subsequently, the accuracy of predicted water saturation in rock types determined by the Lucia method, with a mean error of 0.09 (v/v), ranked second among the rock typing methods. Winland R35 and flow zone indicator methods exhibited the highest uncertainty in water saturation estimation, with mean differences of 0.23 and 0.21 (v/v), respectively, compared to the Dean-Stark method.

Investigating the Impact of Random Field Element Size on Soil Slope Reliability Analysis

The determination of the optimal random field element (RFE) size is crucial in soil slope reliability analysis as it governs the trade-off between precision in failure probability calculations and computational efficiency. Given the substantial computational burden associated with smaller RFE sizes, studies on their impact on slope failure probability are scarce. This research examines the influence of RFE size on failure probability and safety factor, employing the Karhunen–Loève expansion to generate random fields and integrating the simplified Bishop method with particle swarm optimization (PSO) to assess slope stability. Through Monte Carlo Simulation (MCS), this study investigates the effects of the ratio of slope height to RFE size (H/De) on slope reliability metrics across two illustrative cases. Results reveal a notable influence of H/De on the distribution of safety factors (Fs) and failure probability (PF), with overestimation observed at smaller H/De ratios. When H/De exceeds 10 for Example 1 and 15 for Example 2, the Fs distribution patterns in both scenarios stabilize significantly, displaying minimal variability. The PF of Example 1 and Example 2 decreases with the increase of H/De and remains basically unchanged when H/De exceeds 10 and 15, respectively. Consequently, a recommended H/De ratio of 20 is proposed based on the analyzed cases, facilitating accurate calculations while mitigating computational overhead.

Quantifying Solar Radiation Over Northern India and Validating with Cropwat for Agricultural and Renewable Energy Applications

Precise computation of global solar energy is crucial for many fields of investigation such as renewable energy, meteorology, and agriculture especially crop water requirement and irrigation requirement. This paper presents the quantification of solar radiation using weather data during the years from 2020-21. The results obtained were validated with the solar radiation estimated with the help of CROPWAT during the year. Statistical and visual approaches were adopted to validate the quantified results. Analysis revealed the high degree of similarity between simulated and CROPWAT determined daily solar radiation during the year 2020-21 over the study area. Very high values of Willmot index of agreement (1.0), Nash-Sutcliffe efficiency (0.999), and Pearson correlation coefficient (0.998), and low values of mean absolute error (0.138), mean bias error (-0.024), and root mean square error (0.182) between them was observed during the year 2020-21 in the area. The minimum solar radiation reaching over the region were observed to be around 5.71 MJ/m2/day during winter season (January) with a normal value of 13.50 MJ/m2/day, while the maximum value of 24.5 MJ/m2/day was observed during the summer season (June) with a normal value of 24.13 MJ/m2/day over the region. The use of these data for further study is recommended as it is worthful quantification over the region.