Accurate Simulation Model Research Articles

Analyzing the use of rigid- or elastic-plastic finite models for the simulating of cutting processes

The simulation modeling of cutting processes represents a powerful scientific instrument for investigating stress-strain and thermodynamic processes in machined materials. Nevertheless, the principal obstacle to the extensive deployment of this scientific approach is inadequate precision of the resulting research outcomes. This is due to the complexity of formalizing the physical and mechanical forming pattern, considering all the dominant factors in building a high-quality cutting model. Additionally, there is a need for professional experience from the researcher to correctly describe the physical model of the material, as well as a logical selection of fracture criteria, among other factors. One of the most significant challenges in ensuring the accuracy of cutting modeling processes is formalization of the description of a rigid-plastic or elasto-plastic FEA model for analyzing the behavior of materials during machining. The article presents a scientifically based argument for the practicality of using these techniques in simulation modeling systems. It also provides practical recommendations for researchers on constructing an accurate FEA simulation model in DEFORM 2D. The conclusions drawn from the analysis of simulation studies of cutting-induced residual stresses for heterogeneous materials are confirmed by experimental investigations.

Integrated DC arc model and DC arc detection approach based on K‐line diagram and spectrum integral difference

AbstractIn the low voltage direct current (LVDC) systems, the occurrence of DC series arc faults poses a significant threat to the safe operation of the system. This paper develops an accurate arc model for the arc fault simulation. The proposed arc model consists the steady‐state impedance, high frequency characteristics, and dynamic characteristics of the arc. Additionally, this paper proposes an arc detection algorithm combining the K‐line diagram and the spectrum integral difference of the arc current in the LVDC systems. This algorithm can identify four circuit states: normal operation, arc fault, switching action, and load mutation, which addresses the challenge of arc fault detection in complex working conditions. The STM32F407 microcontroller is utilized to design a DC series arc fault detector. Online detection tests demonstrate that the arc faults can be accurately detected and isolated within 37 ms, meeting the requirement of the UL1699B standard, with an accuracy rate of 99.33%. These achievements not only enhance the safety of the LVDC systems but also provide valuable references for the development of arc fault detection technology.

Simulation of Load–Sinkage Relationship and Parameter Inversion of Snow Based on Coupled Eulerian–Lagrangian Method

The accurate calibration of snow parameters is necessary to establish an accurate simulation model of snow, which is generally used to study tire–snow interaction. In this paper, an innovative parameter inversion method based on in situ test results is proposed to calibrate the snow parameters, which avoids the damage to the mechanical properties of snow when making test samples using traditional test methods. A coupled Eulerian–Lagrangian (CEL) model of plate loading in snow was established; the sensitivity of snow parameters to the macroscopic load–sinkage relationship was studied; a plate-loading experiment was carried out; and the parameters of snow at the experimental site were inverted. The parameter inversion results from the snow model were verified by the experimental test results of different snow depths and different plate sizes. The results show the following: (1) The material cohesive, angle of friction, and hardening law of snow have great influence on the load–sinkage relationship of snow, the elastic modulus has a great influence on the unloading/reloading stiffness of snow, and the influence of density and Poisson’s ratio on the load–sinkage relationship can be ignored. (2) The correlation coefficient between the inversion result and the matching test data is 0.979, which is 0.304 higher than that of the initial inversion curve. (3) The load–sinkage relationship of snow with different snow depths and plate diameters was simulated by using the model parameter of inversion, and the results were compared with the experimental results. The minimum correlation coefficient was 0.87, indicating that the snow parameter inversion method in this paper can calibrate the snow parameters of the test site accurately.

Modeling water balance components of conifer species using the Noah-MP model in an eastern Mediterranean ecosystem

Abstract. Few studies have investigated the performance of land surface models for semiarid Mediterranean forests. This study aims to parameterize and test the performance of the Noah-MP land surface model for an eastern Mediterranean ecosystem. To this end, we calibrated the model for root zone soil moisture and transpiration of two conifer species, Pinus brutia, and Cupressus sempervirens, in a plantation forest on the Mediterranean island of Cyprus. The study area has a long-term average annual rainfall of 315 mm. Observations from 4 sap flow and 48 soil moisture sensors, for the period from December 2020 to June 2022, were used for model parameterization. A local sensitivity analysis found that the surface infiltration (REFKDT), hydraulic conductivity (SATDK), and stomatal resistance (RSMIN) parameters had the highest impacts on the water balance components (soil evaporation, tree transpiration, surface runoff, and drainage). The model performed better during the wetter 9-month validation period (379 mm rain) than during the drier 10-month calibration period (175 mm rain). Average soil moisture in the top 60 cm of the soil profile was reasonably well captured for both species (daily Nash–Sutcliffe efficiency > 0.80 for validation). Among the three soil layers, the second layer (20–40 cm) showed better simulation performance during both periods and for both species. The model exhibited limitations with respect to simulating transpiration, particularly during the drier calibration period. The inclusion of a root distribution function in the model, along with the monitoring of soil moisture below the 60 cm soil depth in the field, could improve the accuracy of model simulations in such water-limited ecosystems.

Dynamic thermal simulation of a tree-shaped district heating network based on discrete event simulation

The computational complexity involved in modelling district heating (DH) networks impedes the integration of network operations into comprehensive DH system studies. We developed a flexible, accurate, and fast dynamic thermal simulation model utilising discrete event simulation (DES). This model is versatile, suitable for any tree-shaped DH network with a central heating plant and can estimate node temperatures and calculate pipe heat losses. The speed of the model is improved via using variable time steps and by incorporating two advanced techniques: lazy evaluation and a customised priority queue. To further improve the computational speed, we developed a technique to eliminate redundant sampling points. This model was tested and demonstrated excellent consistency with actual measurements. Remarkably, reducing sampling points can speed up the simulation by a factor of three without compromising the temperature accuracy. A 72-day simulation of a network with 102 pipes was completed within 0.219 s. Our findings highlight the significant potential of the DES model for large-scale dynamic network simulations and offer a promising solution for DH network simulations and system optimisation.

Urban Traffic Simulation with Shared Mobility Services: An Approach Using Spatiotemporal Network Kernel Density Estimation and MATSim

The popularity of smartphones and 4G/5G network have enabled various novel transportation modes based on shared mobility, such as app-based ride-hailing services (e.g., Uber and Lyft) and shared micromobility services (e.g., Veo and Gotcha). However, little is known about to what degree their operations impact urban traffic, which is important for transportation planning and policy making. These companies seldom share their ride data due to business and user privacy reasons, and they are young and still exploring and growing their market to new locations. Recently, transportation engineering researchers began to collect data in large cities trying to understand the transportation impacts of shared mobility services, but (1) there does not exist a general analytics framework applicable to any city, and (2) the studies were based on historical data and cannot project the future easily to catch up with the rapid development of shared mobility services. In this paper, we introduce a general framework for multi-modal urban traffic analytics. The goal is to build a digital twin of the transportation in a city, i.e., an accurate agent-based transportation simulation model, based on a medium-sized dataset of the interested transportation modes collected by the research group combined with other open data sources such as US Census Bureau. With this digital twin, transportation engineering researchers can flexibly analyze the impact of shared mobility services under different scenarios, such as “if the number of Uber drivers doubles” or “if the number of deployed e-scooters doubles”. The digital twin may also enable new opportunities, such as being an environment for learning policies with reinforcement learning. Our framework consists of three steps: (1) fitting the spatiotemporal distribution of the shared mobility rides on the underlying road network, (2) generating the travel day-plans of the entire urban population based on the learned spatiotemporal ride distribution and user-specified parameters, and (3) configuring an agent-based simulation software such as MATSim to execute the generated realistic day-plans, which provides detailed transportation data for analytics. At the core of our approach is a new spatiotemporal network kernel density estimation (KDE) method that fixes the flaw of prior methods where the contributions of different data samples are not equal. We also propose a crowdsourcing method to collect app-based ride data that is easy to carry out in any city. Using Birmingham, AL as an example, we demonstrate how our framework can be applied to help transportation engineering researchers analyze the impacts of Uber ride-hailing and Veo e-scooter services.

A novel sim2real reinforcement learning algorithm for process control

While reinforcement learning (RL) has potential in advanced process control and optimization, its direct interaction with real industrial processes can pose safety concerns. Model-based pre-training of RL may alleviate such risks. However, the intricate nature of industrial processes complicates the establishment of entirely accurate simulation models. Consequently, RL-based controllers relying on simulation models can easily suffer from model-plant mismatch. On the one hand, utilizing offline data for pre-training of RL can also mitigate safety risks. However, it requires well-represented historical datasets. This is demanding because industrial processes mostly run under a regulatory mode with basic controllers. To handle these issues, this paper proposes a novel sim2real reinforcement learning algorithm. First, a state adaptor (SA) is proposed to align simulated states with real states to mitigate the model-plant mismatch. Then, a fix-horizon return is designed to replace traditional infinite-step return to provide genuine labels for the critic network, enhancing learning efficiency and stability. Finally, applying proximal policy optimization (PPO), the SA-PPO method is introduced to implement the proposed sim2real algorithm. Experimental results show that SA-PPO improves performance in MSE by 1.96% and in R by 21.64% on average for roasting process simulation. This verifies the effectiveness of the proposed method.

Mechanical performance analysis method for ribbed H-section aluminum alloy members with initial curvature and torsion angle

In this study, an experimental investigation was conducted on the axial compression performance of ribbed H-section aluminum alloy members with initial curvature and torsion angle under varying boundary conditions, including one end hinged with the other rigidly connected, and both ends rigidly connected. Ultimate bearing capacity and failure modes were identified under real loads and subsequently compared with previous findings from our research group on members with hinged ends. To account for initial imperfections introduced during processing and transportation, 3D scanning technology was utilized to capture the precise geometrical dimensions, constructing an accurate numerical simulation model. The experimental results were corroborated with numerical simulations, leading to the proposal of an analytical method for members with initial curvature and torsion angle. Furthermore, extensive parametric analysis elucidated the impact of initial curvature, torsion angle, and slenderness ratio on the ultimate bearing capacity, culminating in the formulation of the stability factor and calculated length factor based on numerical outcomes. The study discovered significant variances in bearing capacity under different boundary conditions, with one-end hinged and one-section rigidly connected, and two-end rigidly connected conditions exhibiting 1.4 and 2.1 times the capacity of the hinged-at-both-ends scenario. Under different boundary conditions, the axial compression members were subjected to flexural-torsional buckling failure. Moreover, when the ultimate bearing capacity was reached, the lower flange of the member and the web near the lower flange appeared obvious buckling phenomenon. The numerical analysis aligned well with experimental data, validating the simulation method's reliability and revealing the stress distribution and evolution during member failure. These findings offer vital theoretical insights and technical support for engineering design and practical applications.

Multi‐operating characteristic analysis and loss optimization of novel large capacity high‐speed permanent magnet generator

AbstractRecently, the development trend of large capacity generator has gradually shifted from electrical excitation generator to permanent magnet generator (PMG). As a result, the large capacity high‐speed permanent magnet generator (HSPMG) with high power density and high efficiency have received widespread attention. This paper aims to analyse the loss optimization of novel large capacity HSPMG under multi‐operating conditions. In terms of the parameter requirements, a 2.5 MW and 7000 rpm HSPMG is designed. To verify the accuracy of simulation model and the rationality of generator design, the output characteristics of large capacity HSPMG which has been combined with prototype experimental data are calculated under both no‐load and load conditions. Based on the load power characteristics of large capacity HSPMG, the optimization range of the magnetization angle under multi‐operating conditions is explored. By analysing the no‐load back electromotive force (EMF) and loss characteristics of large capacity HSPMG, the optimal magnetization angle that corresponds to the minimum loss under three conditions is obtained. The correctness of the analysis and optimization method is verified by comparing the loss distribution and electromagnetic results before and after the optimization. The research results provide a reference for the multi‐operating characteristics analysis and loss optimization of large capacity HSPMG.

Using QALYs as an Outcome for Assessing Global Prediction Accuracy in Diabetes Simulation Models.

(1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q2, the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and R2. We simulated all EXSCEL trial participants (N = 14,729) using the UK Prospective Diabetes Study Outcomes Model software versions 1 (UKPDS-OM1) and 2 (UKPDS-OM2). The EXSCEL trial compared once-weekly exenatide with placebo (median 3.2-y follow-up). Default UKPDS-OM2 utilities were used to estimate undiscounted QALYs over the trial period based on the observed events and survival. These were compared with the QALYs predicted by UKPDS-OM1/2 for the same period. UKPDS-OM2 predicted patients' QALYs more accurately than UKPDS-OM1 did (MSE: 0.210 v. 0.253; Q2: 0.822 v. 0.786). UKPDS-OM2 underestimated QALYs by an average of 0.127 versus 0.150 for UKPDS-OM1. UKPDS-OM2 predictions were more accurate for mortality, myocardial infarction, and stroke, whereas UKPDS-OM1 better predicted blindness and heart disease. Q2 facilitated comparisons between subgroups and (unlike R2) was lower for biased predictors. Q2 for QALYs was useful for comparing global prediction accuracy (across all clinical events) of diabetes models. It could be used for model registries, choosing between simulation models for economic evaluation and evaluating the impact of recalibration. Similar methods could be used in other disease areas. Diabetes simulation models are currently validated by examining their ability to predict the incidence of individual events (e.g., myocardial infarction, stroke, amputation) or composite events (e.g., first major adverse cardiovascular event).We introduce Q2, the proportional reduction in error, as a measure that may be useful for evaluating and comparing the prediction accuracy of econometric or simulation models.We propose using the Q2 or mean squared error for QALYs as global measures of model prediction accuracy when comparing diabetes models' performance for health technology assessment; these can be used to select the most accurate simulation model for economic evaluation and to evaluate the impact of model recalibration in diabetes or other conditions.

Experimental and numerical study on the chloride ions penetration in recycled aggregate concrete

Recycled aggregate concrete (RAC) offers a practical solution for improving resource efficiency and reducing environmental impact. Durability degradation in concrete structures is primarily caused by the corrosion of internal reinforcement due to chloride ion penetration. Thus, the resistance of RAC to chloride ion penetration is a crucial durability indicator. This study investigates the effects of recycled coarse aggregate (RCA) and mineral powder on RAC's chloride penetration resistance using rapid chloride ions migration (RCM) tests. Results show that increasing the RCA replacement ratio reduces its resistance to chloride ions penetration. The addition of slag improves resistance to chloride ions of RAC by up to 30 %, while fly ash addition shows an initial increase followed by a decline in resistance to chloride ions penetration of RAC. To address the variability in the spatial distribution and thickness of old mortar within RCA, a two-dimensional random aggregate model incorporating ellipsoids and internal polygons was developed. Five distinct phases were considered in the model: natural aggregate, new motar, old mortar, old interface transition zone (ITZ) between natural aggregate and old mortar, new ITZ between old mortar and new mortar. The model simulated the random placement of aggregates by randomizing geometric shapes, considering factors such as the RCA replacement ratio and particle size. COMSOL software was used to simulate chloride ions penetration in RAC with 30 % RCA, and the simulation model's accuracy was verified through ion chromatography. This study takes into account the random distribution of old mortar on the surfaces of RCA to provide a deeper investigation into chloride transport within RAC.

Development of an improved mass transfer model for condensation with dynamic feedback regulation

In the field of condensation research, accurate and efficient numerical simulation models are highly desired. One of the widely used phase change mass transfer models is known as the Lee model. However, this model has limitations due to uncertainties in the mass transfer intensity factor, which can lead to simulation failures and impede large-scale engineering applications. This study presents an improved mass transfer model which employs a tuning function to dynamically and accurately modify the mass transfer intensity factor in each two-phase cell via feedback adjustment. This approach enables immediate and customized adjustment of the factor for each grid within the limit of the tuning function, significantly reducing the errors caused by repeated artificial adjustments and related uncertainties. The adjustment capabilities of the hyperbolic tangent and softsign functions are evaluated. The softsign function is superior in both regulation efficiency and accuracy. The model's accuracy is verified by comparing with previous experiments and simulations, as well as by the one-dimensional Stefan problem. The proposed model achieves high prediction accuracy, reduces the risk of simulation dispersion, and significantly enhances computational efficiency with the iteration number reduced by nearly half under certain conditions compared to the Lee model. Furthermore, the new model shows robustness as the prediction accuracy is insensitive to variations in critical parameters. The improved model is expected to provide valuable assistance for future research and applications in flow condensation with outstanding computational accuracy, efficiency, and stability.

Optimization of an N2O Emission Flux Model Based on a Variable-Step Drosophila Algorithm

The application of intelligent process-based crop model parameter optimization algorithms can effectively improve both the model simulation accuracy and applicability. Based on measured values of soil N2O emission flux in wheat fields from 2020 to 2022, and meteorological data from 1971 to 2022, five parameters of the N2O emission flux module in the APSIM model were optimized using the variable step Fruit Fly algorithm (VSS-FOA). The optimized parameters were the soil nitrification potential, the range of concentrated KNH4 of ammonia and nitrogen at semi-maximum utilization efficiency, the proportion of nitrogen loss to N2O during the nitrification process, the denitrification coefficient, and the Power term P for calculating the denitrification water coefficient. Contrasting the optimized parameters using the VSS-FOA algorithm versus the default values supplied with the model substantially improved the goodness-of-fit to field measurements with the overall R2 increasing from 0.41 to 0.74, and a decrease in NRMSE from 17.1% to 11.4%. This work demonstrates that the VSS-FOA algorithm affords a straightforward mechanism for the optimization of parameters in models such as APSIM to enhance the accuracy of model N2O emission flux estimates.

Evaluation of atmospheric moisture transport to the Tibetan Plateau from 33 CMIP6 models

Atmospheric moisture transport is pivotal in regulating water resources over the Tibetan Plateau (TP). With the growing concerns about climate change, understanding the evolution of atmospheric moisture transport over the TP has become increasingly critical. however, the spatiotemporal distinctions of this transport remain poorly understood in the CMIP6 models. Here, we conducted a comprehensive evaluation of simulated historical atmospheric moisture transport from 33 CMIP6 models, utilizing a novel methodology that assesses the accuracy of model simulations in replicating regional atmospheric moisture transport over the TP. Our results indicate that the CMIP6 models generally succeed in reproducing the broad spatial patterns of atmospheric moisture transport. Nonetheless, substantial errors occur during the monsoon period, primarily attributable to inaccuracies in the location, movement, and intensity of the simulated Indian summer monsoon. The coarser resolution and poor representation of physical processes are potential reasons for errors in atmospheric moisture transport simulation over the TP. The Failure to simulate the terrain blocking on atmospheric moisture transport exacerbates these deficiencies, leading to significant discrepancies. Of the 33 CMIP6 models we investigated, over one-third displayed serious deficiencies in this regard. While coarser resolution and orographic gravity waves are plausible factors, they do not fully account for all the results obtained in this study. Insufficiently detailed or inaccurate topographic data used in the models may also contribute to this deficiency. This study highlights the necessity of using rigorously evaluated models to develop effective regional adaptation strategies over the Tibetan Plateau.

Study on the 2D equivalent nonlinear dynamics simulation model related to high speed precision bearing and spindle

The stiffness and contact stress of the bearing spindle system are the key factors affecting its machining accuracy and service life under the condition of high-speed service, which will be affected by different structural parameters and service conditions. It is essential to establish an efficient and accurate computational simulation model to understand the influence of complex factors on the nonlinear dynamic characteristics of the bearing spindle system. Firstly, in the paper, a 2D axisymmetric finite element model of the bearing, aims at the relationship between stiffness and contact stress of the bearing under high-speed service, has been built based on a classical dynamic analysis model and the reversed method of material parameters of equivalent rolling balls. Additionally, for the BT30 spindle, the 2D axisymmetric finite element model of the bearing spindle system also has been built and applied in mechanical analysis of spindle under different conditions of assembly and service, based on the bearing model. The results show that the axial force of bearings decreases as the rotational speed increases, and an augmentation in speed will result in a reduction in the axial stiffness of the BT30 spindle. In addition, the maximum contact stress exhibits a slight decline as the rotational speed increases. Furthermore, with an escalating preload, the stiffness and contact stress of the spindle undergo substantial increments, however, these parameters will cease to alter once a certain threshold is reached.

Optical Investigation of Sparks to Improve Ignition Simulation Models in Spark-Ignition Engines

The use of renewable fuels in place of fossil fuels in internal combustion engines is regarded as a viable method for achieving zero-impact-emission powertrains. However, to achieve the best performance with these fuels, these engines require further optimization, which is achieved through new combustion strategies and the use of advanced ignition systems such as prechambers. Since simulations greatly accelerate this development, accurate simulation models are needed to accurately predict the combustion phenomenon, which requires a deep understanding of the ignition phenomenon as it significantly affects combustion. This work presents a comprehensive experimental methodology to study sparks under engine conditions, providing quantitative data to improve and validate ignition simulation models. The goal was to determine the volume generated by sparks under engine conditions that can initiate combustion and use this information to improve simulation results to match the experimental results. The visible sparks were observed with high-speed cameras to understand their time-resolved evolution and interaction with the flow. The heat transfer from the plasma was also visualized using a modified Background-Oriented Schlieren technique. The information gained from the experimental observations was used to improve an ignition simulation model. Since the velocity of the plasma was found to be slower than the surrounding flow, a user-defined parameter was included to calibrate the velocity of the simulated plasma particles. This parameter was calibrated to match the simulated spark length to the experimental spark length. In addition, since the previous simulation model did not take the heat transfer from the plasma into account, the simulated plasma particles were coupled to have heat transfer to the surroundings. Based on a comparison of the simulation results with the experimental results, the improved approach was found to provide a better physical representation of the spark ignition phenomenon.

A Stacking Ensemble Learning Model Combining a Crop Simulation Model with Machine Learning to Improve the Dry Matter Yield Estimation of Greenhouse Pakchoi

Crop models are instrumental in simulating resource utilization in agriculture, yet their complexity necessitates extensive calibration, which can impact the accuracy of yield predictions. Machine learning shows promise for enhancing yield estimations but relies on vast amounts of training data. This study aims to improve the pakchoi yield prediction accuracy of simulation models. We developed a stacking ensemble learning model that integrates three base models—EU-Rotate_N, Random Forest Regression and Support Vector Regression—with a Multi-layer Perceptron as the meta-model for the pakchoi dry matter yield prediction. To enhance the training dataset and bolster machine learning performance, we employed the EU-Rotate_N model to simulate daily dry matter yields for unsampled data. The test results revealed that the stacking model outperformed each base model. The stacking model achieved an R² value of 0.834, which was approximately 0.1 higher than that of the EU-Rotate_N model. The RMSE and MAE were 0.283 t/ha and 0.196 t/ha, respectively, both approximately 0.6 t/ha lower than those of the EU-Rotate_N model. The performance of the stacking model, developed with the expanded dataset, showed a significant improvement over the model based on the original dataset.

Calibration and Validation of Simulation Parameters for Maize Straw Based on Discrete Element Method and Genetic Algorithm-Backpropagation.

There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett-Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32-0.36, 1.24 × 108-1.72 × 108 Pa, and 5.9 × 106-6.7 × 106 N/m3, respectively. Using the experimental data of the central composite design as the dataset, a GA-BP neural network prediction model for the peak compression force of maize straw was established, analyzed, and evaluated. The GA-BP prediction model's accuracy was verified via experiments. It was found that the ideal combination of parameters was a Poisson ratio of 0.357, a shear modulus of 1.511 × 108 Pa, and a normal stiffness of 6.285 × 106 N/m3 for the maize straw. The results provide a basis for analyzing the damage mechanism of maize straw during the grinding process.

Machine Learning-based Prediction of Cattle Body Weight Using Muzzle Morphometrics

Body weight measurement of cattle is a tedious farm operation but is essential for their health maintenance at farm. This study proposes a novel and easier approach for cattle body weight prediction using muzzle morphometrics. Vrindavani crossbred cattle of different age groups were considered for the study. The muzzle images were collected and analyzed in MATLAB for determination of muzzle dimensions followed by mapping the dimensions to the body weight of the cattle using artificial neural network with varying network parameters. The results of the showed that all muzzle parameters had good correlation with the body weight of the cattle. Further, it was also observed that the combination of Levenberg-Marquardt training algorithm with logsigmoidal transfer function performed the best with model simulation accuracy of 78.07%. The study concludes that muzzle morphometrics may be used for body weight measurements, however, newer or diverse muzzle parameters may be considered in future works to further improve the model accuracy for a more practical application.

Comparison of in situ black-lipped oyster spat collection and larval dispersal modelling results in semi-closed pearl-farming lagoons of the Tuamotu Archipelago

Spat collection of the pearl oyster Pinctada margaritifera in atoll lagoons of French Polynesia is the fundamental sustain of black pearl farming. Spat collection has always yielded variable results in space and time, but obvious signs of steady decreases, even collapses, have emerged in several lagoons. Spat collection materializes the ecological connectivity pathways between wild spawning populations and the location of artificial larval settlement substrates. To assess if oyster larval dispersal modelling could capture such pathways, we compared four six-week long spat collector deployment periods with dispersal simulations in two different lagoons. Spat collectors displayed wide spatial and temporal variations. Numerical modelling and field experiments were generally not in agreement. Although both methods have limitations, they can still approximate each other. But the accuracy of model simulations cannot be ascertained with spat collection data only. Using a SWOT (Strength-Weakness-Opportunities-Threats) analysis, we emphasize the complementarity of both approaches for management decisions.