Muscle Tendon Passive Parameter Estimation Using Musculoskeletal Optimal Control.

The first lactation weekly test day milk yield (WTDMY) records (57233) and monthly test day milk yield (MTDMY) records (11970) spread over a period of 23 years (1984– 2006) sired by 82 bulls of Karan Fries cattle were used to develop the best lactation curve model. The lactation curve parameters of Quadratic cum log model (QCLM), Gamma function (GF), Cobby Le Du model (CLDM), Polynomial regression function (PRF) and Multiphasic logistic function (MLF) were estimated. The average weekly test day milk yield was predicted with high degree of accuracy (R2 > 85%) by all the models with the maximum accuracy (R2 = 99.50%) obtained by polynomial regression function (PRF) and the least fit (R2 = 87.90%) was obtained by Gamma function (GF). However Quadratic cum log model (R2 = 99.20%) was almost equal to polynomial regression function and was better than Cobby Le Du model (R2 = 92.80%). The average root mean square error (RMSE) was found to be minimum with PRF (0.0121 Kg) followed by MLF (Triphasic). Thus the best fit model was polynomial regression function, which was better than other functions for prediction of first lactation WTDMY. The average monthly test day milk yield was predicted with high degree of accuracy (R2 > 75%) by all the models with the maximum accuracy (R2 > 99.41%) obtained by polynomial regression function (PRF) and the least fit was obtained with gamma function (R2 = 79.05%). However, quadratic-cum-log model (R2 = 99.28%) was almost equal to polynomial regression function and was better than CLDM (R2 = 89.50%). The average root mean square error (RMSE) was found to be minimum with PRF (0.0061 kg) followed by QCLM (0.0620). Thus the best fit model was polynomial regression function for prediction of both weekly and monthly test day milk yield of first lactation in Karan Fries cows.

Phenological shifts in plant species are one of the most conspicuous signs of climate change impact on the biosphere. Modeling phenological variations of plant species over broad regions is challenging because of the varied climatic requirements of geographic populations due to local adaptation. In this study, we developed an empirical method to calibrate phenological models of temperate trees using latitude as a predictor to account for local adaptation of populations to a N-S temperature gradient. Fourteen widely distributed tree species in the eastern U.S.A. were investigated using data from the USA-National Phenology Network. We implemented the method in a basic thermal time bud break model to introduce the algorithm of the method and test its effectiveness. For each species, dates of breaking leaf buds were first predicted using a traditional non-spatial model and then with a spatial model that has the critical thermal forcing requirements calibrated for different populations at varied latitudes. As anticipated, non-spatial model predictions that assumed a uniform forcing requirement across latitudes showed consistent and systematic biases at both higher (overestimation-predictions being later) and lower (underestimation-predictions being earlier) latitudes. Spatial models that have been calibrated using our method removed the geographic biases and yielded latitudinal gradients that more closely matched those of the observations. The spatial models also reduced the overall prediction errors from an average root mean square error (RMSE) of 32.2 days to 20.4 days for the training dataset and an average root mean square error for prediction (RMSEP) of 32.2 days to 19.9 days for the testing dataset. This paper is focused on introducing the new calibration method as a preparatory step toward developing operational models that may potentially predict large-scale and range-wide phenological responses of various plant species to climatic changes with improved local accuracy.

ABSTRACTUpdating of reservoir models by history matching of 4D seismic data along with production data gives us a better understanding of changes to the reservoir, reduces risk in forecasting and leads to better management decisions. This process of seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively when using automated inversion. Observed seismic data is often obtained as a relative measure of the reservoir state or its change, however. The data, usually attribute maps, need to be calibrated to be compared to predictions. In this paper we describe an alternative approach where we normalize the data by scaling to the model data in regions where predictions are good. To remove measurements of high uncertainty and make normalization more effective, we use a measure of repeatability of the monitor surveys to filter the observed time‐lapse data.We apply this approach to the Nelson field. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data which consist of attributes representing measured acoustic impedances and predictions from the model. Two regression equations are derived as part of the analysis. For one, the whole 4D signature map of the reservoir is used while in the second, 4D seismic data is used from the vicinity of wells with a good production match. The repeatability of time‐lapse seismic data is assessed using the normalized root mean square of measurements outside of the reservoir. Where normalized root mean square is high, observations and predictions are ignored. Net: gross and permeability are modified to improve the match.The best results are obtained by using the normalized root mean square filtered maps of the 4D signature which better constrain normalization. The misfit of the first six years of history data is reduced by 55 per cent while the forecast of the following three years is reduced by 29 per cent. The well based normalization uses fewer data when repeatability is used as a filter and the result is poorer. The value of seismic data is demonstrated from production matching only where the history and forecast misfit reductions are 45% and 20% respectively while the seismic misfit increases by 5%. In the best case using seismic data, it dropped by 6%. We conclude that normalization with repeatability based filtering is a useful approach in the absence of full calibration and improves the reliability of seismic data.

Leaf biochemical traits indicating early symptoms of plant stress can be assessed using imaging spectroscopy combined with radiative transfer modelling (RTM). In this study, we assessed the potential applicability of the leaf radiative transfer model Fluspect-Cx to simulate optical properties and estimate leaf biochemical traits through inversion of two native Australian eucalypt species: Eucalyptus dalrympleana and E. delegetensis. The comparison of measured and simulated optical properties revealed the necessity to recalibrate the refractive index and specific absorption coefficients of the eucalypt leaves’ biochemical constituents. Subsequent validation of the modified Fluspect-Cx showed a closer agreement with the spectral measurements. The average root mean square error (RMSE) of reflectance, transmittance and absorptance values within the wavelength interval of 450–1600 nm was smaller than 1%. We compared the performance of both the original and recalibrated Fluspect-Cx versions through inversions aiming to simultaneously retrieve all model inputs from leaf optical properties with and without prior information. The inversion of recalibrated Fluspect-Cx constrained by laboratory-based measurements produced a superior accuracy in estimations of leaf water content (RMSE = 0.0013 cm, NRMSE = 6.55%) and dry matter content (RMSE = 0.0036 g·cm−2, NRMSE = 21.28%). The estimation accuracies of chlorophyll content (RMSE = 8.46 µg·cm−2, NRMSE = 24.73%), carotenoid content (RMSE = 3.83 µg·cm−2, NRMSE = 30.82%) and anthocyanin content (RMSE = 1.69 µg·cm−2, NRMSE = 37.12%) were only marginally better than for the inversion without any constraints. Additionally, we investigated the possibility to substitute the prior information derived in the laboratory by non-destructive reflectance-based spectral indices sensitive to the retrieved biochemical traits, resulting in the most accurate estimation of carotenoid content (RMSE = 3.65 µg·cm−2, NRMSE = 29%). Future coupling of the recalibrated Fluspect with a forest canopy RTM is expected to facilitate retrieval of biophysical traits from spectral air/space-borne image data, allowing for assessing the actual physiological status and health of eucalypt forest canopies.

Empirical ocean color algorithms and bio-optical properties of the western coastal waters of Svalbard, Arctic

Application of Landsat-8 and Sentinel-2 for retrieval of chlorophyll-a in a shallow freshwater lake

Background/Purpose: The acquisition of three-dimensional ground reaction forces (3D-GRF) during running is crucial for monitoring and assessing running performance. However, limitations often arise in collecting 3D-GRF due to equipment and environmental constraints. Inertial sensors are gaining popularity in running applications because of their convenience, broad applicability, and cost-effectiveness. This study aimed to utilize an inertial sensor for gathering kinematic data during running, establish a high-precision neural network model for continuous 3D-GRF estimation, and investigate estimation variances across different body segments. Method: A total of thirty-three college students (17 male, 16 female) participated in this study. Kinematic and kinetic data were concurrently recorded using inertial sensors placed on the lower back, lateral thigh, anterior lower leg, and dorsum of the foot, while three-dimensional force plates were utilized during running at speeds of 8/10/12 km/h. The dataset was partitioned into training, validation, and test sets at a ratio of 24:3:6. A Long Short-Term Memory Neural Network (LSTM) was developed for training to predict 3D-GRF. Evaluation metrics included coefficients of determination, Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). Models customized for specific body segments underwent a nine-fold cross-validation procedure, employing one-way ANOVA to evaluate estimation variances across different anatomical sites. Results: Estimation outcomes at different sites indicated optimal estimation accuracy at the right thigh. The coefficients of determination for Anteroposterior (AP)-GRF, Mediolateral (ML)-GRF, and Vertical (V)-GRF were 0.81±0.00, 0.55±0.14, and 0.97±0.02, respectively. RMSE values were 0.06±0.01BW, 0.06±0.01BW, and 0.14±0.03BW. Significant variances were observed between the right thigh and other sites in the horizontal plane (p < 0.05). The results from the test set showed correlation coefficients, RMSE, and NRMSE for AP-GRF, ML-GRF, and V-GRF: AP-GRF, 0.94±0.01, 0.06±0.01BW, 4.78%±0.50%; ML-GRF, 0.83±0.08, 0.10±0.01BW, 9.70%±2.41%; V-GRF, 0.99±0.00, 0.14±0.02BW, 2.79%±0.24%. Conclusion/Discussion: 1. The neural network model developed in this study demonstrated exceptional accuracy across three dimensions, facilitating continuous estimation of 3D ground reaction forces during running. Correlation coefficients surpassed 0.8 in every direction, with RMSE values below 0.2BW and NRMSE below 5%. 2. Estimation results from inertial sensors positioned at different sites exhibited diverse levels of disparity. The lateral thigh emerged as the most proficient location for estimation, with the lower back excelling in vertical ground reaction force estimation but displaying comparatively weaker performance in horizontal ground reaction force estimation.

Time-varying wind conditions, on which wind turbines depend, significantly influence power generation performance. Accordingly, this study proposes an approach to improve the accuracy of wind power generation prediction by incorporating time-varying wind conditions into the OpenFAST wind turbine model. An OpenFAST wind turbine model was constructed using wind speed and wind direction data collected from the weather station near the wind farm. The numerical model was composed of cases where only wind speed was considered and cases where the time-varying wind direction was considered. And the hourly and daily power generation prediction results were compared and analyzed with the actual power generation through indicators such as correlation coefficient, RMSE (Root Mean Square Error) and NRMSE (Normalized Root Mean Square Error). As a result, in the model that reflects time-varying wind direction, errors were reduced and linear correlation was improved, both in comparison with actual power generation. Therefore, it can be concluded that this model enhances the accuracy of power generation prediction. Consequently, this study highlights the importance of considering time-varying wind direction in OpenFAST wind turbine simulation.

A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing

Leaf nitrogen concentration (LNC) of winter wheat can reflect its nitrogen (N) status. Rapid, non-destructive and accurate monitoring of LNC of winter wheat has important practical applications in monitoring N nutrition and fertilizing management. The experimental site of winter wheat was located at Xiaotangshan National Demonstration Base of Precision Agricultural Research located in Changping District, Beijing, China. High spatial resolution digital images of the winter wheat were acquired using a low-cost unmanned aerial vehicle (UAV) with digital camera system at three key growth stages of booting, flowering and filling during April to June in 2015. Firstly, the acquired UAV digital images were mosaicked to generate a Digital Orthophoto Map (DOM) of the entire experimental site and 15 digital image variables were constructed. Then, based on the ground measured data onto LNC and digital image variables derived from the DOM for 48 sampling plots of winter wheat, linear and stepwise regression models were constructed for estimating LNC. Finally, the optimum model for estimating LNC was screened out by comprehensively considering the coefficient of determination (R2), the root mean square error (RMSE), the normalized root mean square error (nRMSE) and the simplicity of model calibrating and validating. The experimental results showed that the linear regression model of r/b that was one of the digital image variables for estimating LNC had the best accuracy with the model’s calibration and validation of R2, RMSE and nRMSE were 0.76, 0.40, 11.97% and 0.69, 0.43, 13.02%, respectively. The results suggest that it is feasible to estimate LNC of winter wheat based on the DOM acquired by UAV remote sensing platform carrying a low-cost, high-resolution digital camera, which can rapidly and non-destructively obtains the LNC of winter wheat experiment site and provide a quick and low-cost method for monitoring N nutrition and fertilizing management.

Multivariable Recursive Subspace Identification with Application to Artificial Pancreas Systems

Travel planning is a process that allows users to obtain maximum benefit from their time, cost and energy. When planning a route from one place to another, it is an important option to present alternative travel areas on the route. This study proposes a travel route planning (TRP) architecture using a Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) model to improve both travel efficiency and environmental sustainability in route selection. This model incorporates carbon emissions directly into the route planning process by unifying user preferences, location recommendations, route optimization, and multimodal vehicle selection within a comprehensive framework. By merging environmental sustainability with user-focused travel planning, it generates personalized, practical, and low-carbon travel routes. The carbon emissions observed with TRP’s artificial intelligence (AI) recommendation route are presented comparatively with those of the user-determined route. XGBoost, Random Forest (RF), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), (Extra Trees Regressor) ETR, and Multi-Layer Perception (MLP) models are applied to the TRP model. LSTM is compared with Recurrent Neural Networks (RNNs) and Gated Recurrent Unit (GRU) models. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Normalized Root Mean Square Error (NRMSE) error measurements of these models are carried out, and the best result is obtained using XGBoost and LSTM. TRP enhances environmental responsibility awareness within travel planning by integrating sustainability-oriented parameters into the decision-making process. Unlike conventional reservation systems, this model encourages individuals and organizations to prioritize eco-friendly options by considering not only financial factors but also environmental and socio-cultural impacts. By promoting responsible travel behaviors and supporting the adoption of sustainable tourism practices, the proposed approach contributes significantly to the broader dissemination of environmentally conscious travel choices.

The quantification of the relationship between morphological and color indicators in various organs of horticultural crops is of great significance for crop digital visualization research using computer vision technology. To study this relationship, observational data from a six-year experiment were collected, focusing on seven kinds of color component values of different organs including root, stem, and leaf. Using the collected color data as input, a simulation model was established based on the Elman neural network for six horticultural crops including zizania, cucumber, celery, spinach, parsley, and tea. Results indicated that the horticultural crop morphology model based on the Elman neural network exhibited high simulation accuracy with root mean square error (RMSE) ranging from 0.14 to 1.05 cm and normalized root mean square error (NRMSE) ranging from 2.02% to 11.34% for the maximum root length simulation model. The simulation model for stem length and diameter had an RMSE ranging from 1.42 to 4.96 cm and 0.25 to 1.17 mm, respectively, with NRMSE ranging from 18.19% to 25.65% and 15.13% to 27.25%, respectively. Similarly, chlorophyll content, leaf length, leaf width, and leaf area simulation models exhibited RMSE ranging from 2.80 to 8.22 SPAD, 0.44 to 18.04 cm, 0.22 to 3.49 cm, and 0.25 to 36.39 cm2, respectively, with NRMSE ranging from 8.63% to 21.04%, 15.00% to 22.87%, 15.12% to 33.58%, and 6.88% to 24.90%, respectively. These findings provide essential theoretical support for precision agriculture in areas of water and fertilizer management, plant growth diagnosis, and yield prediction. Keywords: horticultural crop, morphology, color, Elman neural network, root, stem and leaf DOI: 10.25165/j.ijabe.20251805.8435 Citation: Cheng C, Lyu Y, Feng L P, Qin H X, Che X Q, Chen S N, et al. Retrieval of horticultural crop morphology from color based on Elman neural network. Int J Agric & Biol Eng, 2025; 18(5): 259–267.

The smart buildings’ load forecasting is necessary for efficient energy management, and it is easily possible because of the data availability based on widespread use of Internet of Things (IoT) devices and automation systems. The information of buildings’ occupancy is directly associated with energy consumption. Therefore, we present a hybrid model consisting of a Long Short-Term Memory (LSTM) network, Extreme Gradient Boosting (XgBoost), Random Forest (RF) and Linear Regression (LR) for commercial and academic buildings’ load forecasting. The correlation between occupants’ count and total load of the building is calculated using Pearson Correlation Coefficient (PCC). The comparative analysis of the proposed approach with LSTM, XgBoost, RF and Gated Recurrent Unit (GRU) is also performed. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Normalized Root Mean Square Error (NRMSE) are used as performance indicators for evaluating performance. Findings indicate that the proposed hybrid approach outperforms other models. The RMSE and MAE of 2.99 and 2.18, respectively, are recorded by the proposed model for commercial building dataset while for academic building the RMSE and MAE are 4.48 and 2.85, respectively. Occupancy and load consumption have a positive correlation as evident from PCC analysis. Therefore, we have scheduled the forecasted load based on occupancy patterns for two different cases. Cost is reduced by 17.42% and 33.40% in case 1 and case 2, respectively. Moreover, the performance of the proposed hybrid approach is compared with different techniques presented in literature for buildings load forecasting.

Kalman filter based on a fractional discrete-time stochastic augmented CoVid-19 model