Weight Estimation Model Research Articles

Prediction of live weight from body measurements using stepwise regresion models in Karacabey Merino lambs

Objective: The aim of this study is to determine regression models that can be used to estimate live weight from body measurements in Karacabey Merino lambs of different ages(6th, 8th, and 12th months).. Material-Method: The animal material for the study consisted of 200 Karacabey merino lambs. Some body measurements were taken from all lambs and live weights were also weighed. For live weight prediction equations with multiple linear regression analysis using some measurements according to age groups, stepwise multiple regression procedure was used in SAS (1999). DUNCAN test, one of the tests for multiple comparisons, was used to show the differences between groups. Result: The least squares means for body length (BL), withers height (HW), back height (BH), rump height (RH), breast depth (CD), breast width (CW), rump width (RW), and live weight (LW) were 71.28 cm, 69.91 cm, 70.50 cm, 71.57 cm, 30.05 cm, 87.25 cm, 21.50 cm, 23.32 cm, and 51.05 kg, respectively. It is noteworthy that the live weight estimation models for three different age groups using stepwise regression analysis(second, third, and fourth models) can be recommended for the 6th (R2:0.82), 8th (R2:0.71), and 12th (R2:0.79) months of life. The variables that can be used in the equations to estimate body weight for this breed, at these ages are HW, CW, RW, CG, CD, and BL. Conclusion: Finally, it has been demonstrated that the live weights of Karacabey merino lambs can be estimated with high accuracy using the stepwise regression method based on body measurements.

Predicting Body Weight in Pre-Weaned Holstein-Friesian Calves Using Morphometric Measurements.

Regularly weighing calves helps to assess the efficiency of the rearing period and contributes to animal welfare by allowing more precise feeding and medication application in dairy farming, but many farmers do not weigh their calves regularly. Improving the feasibility of this process is, therefore, important. The use of morphometric measurements has been used to estimate the weight of cattle. However, many studies have focused on adult animals or used a wide age range. As calves experience allometric tissue growth, specific models for certain ranges might be more accurate. Therefore, the aim of this work was to develop a weight estimation model specific for pre-weaned Holstein-Friesian calves using morphometric measurements and to compare the model with another equation previously validated for the same breed with young and adult animals. From four dairy farms, 237 measurements of body weight, heart girth, abdominal girth, hip height, withers height, and body length were taken from Holstein-Friesian male and female calves. Linear and non-linear regression analysis was used to test the relationship between body weight and morphometric measurements, with age, sex, and farm as possible explanatory variables. Selected models were compared with goodness of fit and agreement tests. The final model was able to accurately predict body weight (R2 = 0.96) with a mean difference of -1.4 ± 3.24 kg. Differences in the relationship between body weight and morphometric traits were observed between farms, but not between males and females. The genetics of the animal population at farm level may be responsible for this variability and further studies are needed to understand this variability and improve weight prediction models. The developed model was able to perform better in the agreement tests than the previously validated model for Holstein-Friesian animals, suggesting that different equations should be used depending on the growth phase the animal is in. In addition, a web application has been developed to facilitate the use of the developed model by farmers. This avoids the use of calibrated weight bands, which are usually calibrated for a broader age range or for beef cattle.

Stereo matching and 3D reconstruction with NeRF supervision for accurate weight estimation in free-swimming fish

Non-invasive methods of accurate estimation of fish weight are essential for biomass assessment, precision feeding, and effective management in aquaculture. However, the lack of large-scale stereo matching datasets and costly depth sensing equipment in underwater scenarios present significant challenges to accurately measure the size of free-swimming fish. This study introduces a novel NeRF-supervised stereo matching network for precise depth estimation and 3D reconstruction of the fish body. The state-of-the-art neural rendering methods are employed to generate stereo training data from image sequences. In this study, the residual network with deformable convolutions was proposed to extract contextual texture features, significantly enhancing disparity estimation accuracy at the edge of the fish body. Additionally, multiscale spatial features are fused using a feature pyramid network (FPN). To enhance the adaptability of disparity search, we introduce a lightweight Triplet Attention module to capture cross-dimensional dependencies prior to constructing the correlation volume. Experimental results demonstrate the effectiveness of the proposed depth estimation method, achieving an impressive result of 92.35% in the similarity metric, compared to the ground truth. Moreover, we developed a weight estimation model based on bass perimeter using instance segmentation and 3D reconstruction techniques. Notably, a robust correlation (correlation coefficient of 0.98) between fish perimeter and weight is observed, highlighting the potential application of our methods to enhance the accuracy of weight estimation for free-swimming fish.

Aircraft Takeoff and Landing Weight Estimation from Surveillance Data

Aircraft weight estimation is a common problem facing researchers working with aircraft surveillance data. Although knowledge of an aircraft’s weight and thrust is required for many types of analyses, such as those evaluating aircraft acoustic noise, fuel burn, and emissions, these parameters are typically not available from surveillance sources. Instead, researchers generally only have access to basic aircraft states: lateral position, groundspeed, and altitude. Therefore, methods for estimating the weight of aircraft from these basic states become necessary in cases where aircraft performance is a key component of the analysis. This paper introduces two weight estimation models: one for the estimation of aircraft takeoff weight from departure data, and another for the estimation of aircraft landing weight from arrival data. The models are mathematically simple but grounded in knowledge of aircraft certification, airline operations, and aircraft flight management system logic. The landing weight estimation model proposed is shown to have a mean absolute error equivalent to 2.66% of maximum takeoff weight and a standard deviation of 3.35% of maximum takeoff weight when validated using onboard data recordings from 240 Airbus A320 flights. Similarly, the proposed takeoff weight estimation model is shown to have a mean absolute error of 2.83% of the maximum takeoff weight and a standard deviation of 3.55% of the maximum takeoff weight when applied to the same validation dataset.

Sampling weighting strategies in causal mediation analysis

BackgroundCausal mediation analysis plays a crucial role in examining causal effects and causal mechanisms. Yet, limited work has taken into consideration the use of sampling weights in causal mediation analysis. In this study, we compared different strategies of incorporating sampling weights into causal mediation analysis.MethodsWe conducted a simulation study to assess 4 different sampling weighting strategies-1) not using sampling weights, 2) incorporating sampling weights into mediation “cross-world” weights, 3) using sampling weights when estimating the outcome model, and 4) using sampling weights in both stages. We generated 8 simulated population scenarios comprising an exposure (A), an outcome (Y), a mediator (M), and six covariates (C), all of which were binary. The data were generated so that the true model of A given C and the true model of A given M and C were both logit models. We crossed these 8 population scenarios with 4 different sampling methods to obtain 32 total simulation conditions. For each simulation condition, we assessed the performance of 4 sampling weighting strategies when calculating sample-based estimates of the total, direct, and indirect effects. We also applied the four sampling weighting strategies to a case study using data from the National Survey on Drug Use and Health (NSDUH).ResultsUsing sampling weights in both stages (mediation weight estimation and outcome models) had the lowest bias under most simulation conditions examined. Using sampling weights in only one stage led to greater bias for multiple simulation conditions.DiscussionUsing sampling weights in both stages is an effective approach to reduce bias in causal mediation analyses under a variety of conditions regarding the structure of the population data and sampling methods.

Banana Bunch Weight Estimation and Stalk Central Point Localization in Banana Orchards Based on RGB-D Images

Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R2 value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots.

Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data.

Pig farming is a crucial sector in global animal husbandry. The weight and body dimension data of pigs reflect their growth and development status, serving as vital metrics for assessing their progress. Presently, pig weight and body dimensions are predominantly measured manually, which poses challenges such as difficulties in herding, stress responses in pigs, and the control of zoonotic diseases. To address these issues, this study proposes a non-contact weight estimation and body measurement model based on point cloud data from pig backs. A depth camera was installed above a weighbridge to acquire 3D point cloud data from 258 Yorkshire-Landrace crossbred sows. We selected 200 Yorkshire-Landrace sows as the research subjects and applied point cloud filtering and denoising techniques to their three-dimensional point cloud data. Subsequently, a K-means clustering segmentation algorithm was employed to extract the point cloud corresponding to the pigs' backs. A convolutional neural network with a multi-head attention was established for pig weight prediction and added RGB information as an additional feature. During the data processing process, we also measured the back body size information of the pigs. During the model evaluation, 58 Yorkshire-Landrace sows were specifically selected for experimental assessment. Compared to manual measurements, the weight estimation exhibited an average absolute error of 11.552 kg, average relative error of 4.812%, and root mean square error of 11.181 kg. Specifically, for the MACNN, incorporating RGB information as an additional feature resulted in a decrease of 2.469 kg in the RMSE, a decrease of 0.8% in the MAPE, and a decrease of 1.032 kg in the MAE. Measurements of shoulder width, abdominal width, and hip width yielded corresponding average relative errors of 3.144%, 3.798%, and 3.820%. In conclusion, a convolutional neural network with a multi-head attention was established for pig weight prediction, and incorporating RGB information as an additional feature method demonstrated accuracy and reliability for weight estimation and body dimension measurement.

Possibilidades de uso da função spline cúbica e de modelos de densidade de produção na estimativa do peso vivo de cordeiros kıvırcık alimentados com diferentes tipos de silagem

ABSTRACT In this study, the possibilities of using Cubic Spline functions and some yield-density models in the estimation of live weights of Kıvırcık lambs fed with different silage types were investigated. In the experiment, 40 male Kıvırcık lambs aged 2.5-3 months were used and the animals were fattened for 56 days. To assess the predictive performance of the fitted models, model fit statistics such as the coefficient of determination (R2), adjusted R2, mean square error (MSE), and Akaike Information Criterion (AIC) were determined. The Cubic Spline model was discovered to be the best model for quantifying Kıvırcık lambs live weight, with the highest R2 and adjusted R2 values, as well as the lowest MSE and AIC values. Cubic Spline functions were applied as live weight estimation model in lambs fed with silage containing 5 different ratios of corn and sunflower (100% sunflower silage, 75% sunflower + 25% corn silage, 50% sunflower + 50% corn silage, 25% sunflower + 75% corn silage, 100% corn silage). As a result, Cubic Spline functions have been found to be effective in estimating the live weight of fattened lambs.

Digital phenotyping for robust seeds variability assessment in Setaria italica (L.) P. Beauv.

ABSTRACT: Foxtail millet (Setaria italica L.) is a cereal crop with potential as a functional food due to its high nutritional value and its wide adaptability to unfavorable environmental conditions. Seed-related traits of foxtail millet are difficult to observe due to its small size. Therefore, the development of an efficient and accurate method for characterizing foxtail millet seeds using digital imaging technology is essential. This study aimed to characterize the seed morphology for variability assessment of Indonesian local foxtail millet genotypes and to develop a model to estimate the 100-seed weight using ImageJ. A total of 28 Indonesian local foxtail millet genotypes were used in this study for seed morphology characterization and the development of the 100-seed weight estimation model. Foxtail millet genotypes from different regions in Indonesia exhibited diverse seed morphologies. The 100-seed weight estimation model (y = 0.123x - 0.0821 (R² = 0.9223)) demonstrated a highly significant positive correlation (r = 0.96, p < 0.01) between the predicted and actual 100-seed weights. The correlation coefficient from model validation was 0.8731 (p < 0.01), indicating that the obtained model could estimate the 100-seed weight of foxtail millet seeds in future studies.

Development of birth weight estimation model for Ethiopian population from sonographic evaluation

BackgroundFetal birth weight (FBW) estimation involves predicting the weight of a fetus prior to delivery. This prediction serves as a crucial input for ensuring effective, accurate, and appropriate obstetric planning, management, and decision-making. Typically, there are two methods used to estimate FBW: the clinical method (which involves measuring fundal height and performing abdominal palpation) or sonographic evaluation. The accuracy of clinical method estimation relies heavily on the experience of the clinician. Sonographic evaluation involves utilizing various mathematical models to estimate FBW, primarily relying on fetal biometry. However, these models often demonstrate estimation errors that exceed acceptable levels, which can result in inadequate labor and delivery management planning. One source of this estimation error is sociodemographic variations between population groups in different countries. Additionally, inter- and intra-observer variability during fetal biometry measurement also contributes to errors in FBW estimation.MethodsIn this research, a novel mathematical model was proposed through multiple regression analysis to predict FBW with an accepted level of estimation error. To develop the model, population data consisting of fetal biometry, fetal ultrasound images, obstetric variables, and maternal sociodemographic factors (age, marital status, ethnicity, educational status, occupational status, income, etc.) of the mother were collected. Two approaches were used to develop the mathematical model. The first method was based on fetal biometry data measured by a physician and the second used fetal biometry data measured using an image processing algorithm. The image processing algorithm comprises preprocessing, segmentation, feature extraction, and fetal biometry measurement.ResultsThe model developed using the two approaches were tested to assess their performance in estimating FBW, and they achieved mean percentage errors of 7.53% and 5.89%, respectively. Based on these results, the second model was chosen as the final model.ConclusionThe findings indicate that the developed model can estimate FBW with an acceptable level of error for the Ethiopian population. Furthermore, this model outperforms existing models for FBW estimation. The proposed approach has the potential to reduce infant and maternal mortality rates by providing accurate fetal birth weight estimates for informed obstetric planning.

A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms

Managing daily nutrition is a prominent concern among individuals in contemporary society. The advancement of dietary assessment systems and applications utilizing images has facilitated the effective management of individuals' nutritional information and dietary habits over time. The determination of food weight or volume is a vital part in these systems for assessing food quantities and nutritional information. This study presents a novel methodology for evaluating the weight of food by utilizing extracted features from images and training them through advanced boosting regression algorithms. Α unique dataset of 23,052 annotated food images of Mediterranean cuisine, including 226 different dishes with a reference object placed next to the dish, was used to train the proposed pipeline. Then, using extracted features from the annotated images, such as food area, reference object area, food id, food category, and food weight, we built a dataframe with 24,996 records. Finally, we trained the weight estimation model by applying cross validation, hyperparameter tuning, and boosting regression algorithms such as XGBoost, CatBoost, and LightGBM. Between the predicted and actual weight values for each food in the proposed dataset, the proposed model achieves a mean weight absolute error 3.93 g, a mean absolute percentage error 3.73% and a root mean square error 6.05 g for the 226 food items of the Mediterranean Greek Food database (MedGRFood), setting new perspectives in food image-based weight and nutrition estimate models and systems.

Pendugaan Bobot Badan Melalui Ukuran Shank pada Ayam Kampung yang Dipelihara Secara Tradisional di Manokwari

This study aims to find an estimator of body weight by means of shank length and shank circumference in adult Kampung chickens reared traditionally. A total of 173 adult Kampung chickens (79 males and 94 females), aged 1 year belonging to several breeders in Prafi District, Manokwari Regency, were used as material for this study. Best Subsets Regression analysis was used to find the best body weight estimation model for this research chicken. The results showed that shank length (PSJ) was the best estimator of body weight (BBJ) for adult male Kampung chickens through the formula: BBJ = 0.363 PSJ – 1.67, because it had the best accuracy rate (highest R2 and lowest C-p and s) compared to the formula others available. For adult female Kampung chickens, shank length (PSB) and shank circumference (LSB) are the best estimators of body weight (BBB) for adult female Kampung chickens using the formula: BBB = 0.205 PSB + 0.127 LSB - 0.864, because it has the best level of accuracy (R2 highest and C-p and lowest s) compared to other available formulas. These formulas are very well applied to Kampung chicken farms that are kept traditionally in an area in order to choose good male candidates and prospective brooders for that area.

Weight estimation models for commercial Pinus radiata wood in small felling stands based on UAV-LiDAR data

Uncrewed Aerial Vehicles (UAVs) equipped with Light Detection and Ranging (LiDAR) scanners have been adopted as an effective tool for conducting forest inventories, reducing field work and associated costs. These studies are based on obtaining information on volume, height and diameter at breast height (DBH). However, in the timber sector in Galicia (Spain)the variable used as the unit of purchase/sale is "weight". Therefore, this paper sets out to test different machine learning algorithms, such as multiple linear regression (MLR), MLR log-transformed (MRL-LT), Principal Component Analysis (PCA) and Random Forest (RF) to obtain a methodology applicable in the study area and replicable in other areas, with real utility in the forestry industry for determining the weight of Pinus radiata wood. These models are based on 73 observations and 19 predictors and have been developed and applied to a total of 33 Forest Cuts (FC) divided into 5204 tesserae. The models are validated by Fold Cross-Validation, with RMSE (relative root mean square error), R2 and MAE (mean absolute error) values being calculated. Of the four models studied, the PCA model performs best (R2=0.85, RMSE=1.84, MAE=1.46), followed by the MRL-LT which gives a value of R2=0.82 (RMSE=1.84, MAE=1.46). The actual figure for total Pinus radiata timber harvested was 35,440.54 t The PCA model estimated a total of 35,913.53 t (r = 0.98, R2=0.97, εr=9.35 %), while the MRL-LT model calculated 35,847.92 t (r = 0.99, R2=0.98, εr=8.85 %). On the other hand, the weight was underestimated by the MLR model and overestimated by the RF. The plain MLR model underestimated the "weight" of the wood, while the MRL-LT model provided the best result, with errors of less than 10 % in 72 % of the FCs. In conclusion, this study provides a powerful tool that will enable stakeholders in the community timber industry to accurately estimate the weight of timber in Pinus radiata stands, enhancing a more highly automated forest inventory that can optimise the number of field operations to be performed.

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network.

The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig's back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.

An orientation independent vision based weight estimation model for Alphonso mangoes

One of the most essential factors in classifying and qualitatively evaluating mangoes for various industrial uses is weight. To meet grading requirements during industrial processing, this paper presents an orientation-independent weight estimation method for the mango cultivar “Alphonso.” In this study, size and geometry are considered as key variables in estimating weight. Based on the visual fruit geometry, generalized hand-crafted local and global features, and conventional features are calculated and subjected to the proposed feature selection methodology for optimal feature identification. The optimal features are employed in regression analysis to estimate the predicted weight. Four regression models –MLR, Linear SVR, RBF SVR, and polynomial SVR—are used during the experimental trials. A self-collected mango database with two orientations per sample is obtained using a CCD camera. Three different weight estimation techniques are used in the analysis concerning orientation 1, orientation 2, and combining both orientations. The SVR RBF kernel yields a higher correlation between predicted and actual weights, and experiments demonstrate that orientation 1 is symmetric to orientation 2. By exhibiting a correlation coefficient of R2 = 0.99 with SVR-RBF for weight estimation using both orientations as well as individual orientations, it is observed that the correlation between predicted and estimated weights is nearly identical

Estimating Body Weight in Captive Rabbits Based on Improved Mask RCNN

Automated body weight (BW) estimation is an important indicator to reflect the automation level of breeding, which can effectively reduce the damage to animals in the breeding process. In order to manage meat rabbits accurately, reduce the frequency of manual intervention, and improve the intelligent of meat rabbit breeding, this study constructed a meat rabbit weight estimation system to replace manual weighing. The system consists of a meat rabbit image acquisition robot and a weight estimation model. The robot stops at each cage in turn and takes a top view of the rabbit through an RGB camera. The images from the robot are automatically processed in the weight estimation model, which consists of the meat rabbit segmentation network based on improved Mask RCNN and the BW fitting network. Attention mechanism, PointRend algorithm, and improved activation function are proposed to improve the performance of Mask RCNN. Six morphological parameters (relative projected area, contour perimeter, body length, body width, skeleton length, and curvature) are extracted from the obtained mask, and are sent into the BW fitting network based on SVR-SSA-BPNN. The experiment shows that the system achieves a 4.3% relative error and 172.7 g average absolute error in BW estimation for 441 rabbits, while the meat rabbit segmentation network achieves a 99.1% mean average precision (mAP) and a 98.7% mean pixel accuracy (MPA). The system provides technical support for automatic BW estimation of meat rabbits in commercial breeding, which is helpful to promote precision breeding.

Измерение массы плода с использованием автоматизированной трехмерной модели фракционного объема конечностей

Objective. To investigate the efficacy of using an automated three-dimensional (3D) fractional limb volume model to calculate estimated fetal weight (EFW) and predict fetal macrosomia. Patients and methods. This prospective observational study included 66 pregnant women who reached 30+0 to 41+3 weeks’ gestation. All patients underwent ultrasonography with fractional thigh volume (TVol) and fractional arm volume (AVol) measurements using 5D Limb Vol software (Samsung Medison W10 ultrasound system). Mean percentage error (MPE) was determined to assess the accuracy of fetal weight estimation. Results. Fractional thigh and arm volumes had a strong correlation with fetal weight (0.834 and 0.617, respectively) and increased linearly with gestational age. When performing a ROC analysis, TVol was found to be a predictor of fetal macrosomia with a sensitivity of 82.35% and specificity of 84.21%, and an experimental model including fetometric parameters (head circumference, abdominal circumference, and femur length) and TVol explained 83% of the variance in birth weight (p = 0.001). In our study, the formulas that included TVol and AVol to calculate EFW were less accurate than the Hadlock formula, with MPE of -1.25 ± 5.75, whereas when using the 3D model with TVol and AVol, MPE was 9.65 ± 7.84 and -9.16 ± 7.63, respectively. However, the experimental model with TVol produced more accurate EFW values compared to the Hadlock formula: the proportion of measurements with an estimated error ≤5% was 94.4% and 100% with an estimated error ≤10% (p < 0.001). Conclusion. Fractional limb volume is an easily reproducible and promising parameter, and its use may be helpful to increase the accuracy of standard fetometry. Fractional thigh volume demonstrated high predictive ability for macrosomia, and its addition to standard fetometric parameters improved the model for fetal weight estimation. Key words: estimated fetal weight, fractional thigh and arm volumes, macrosomia, 5D Limb VolTM

Production planning decisions in the broiler chicken supply chain with growth uncertainty

Broiler chickens are specially raised for meat production and their supply chain is composed of breeders, hatcheries, feed mills, farms, slaughterhouses, wholesalers, and retailers. The coordination between supply chain players has great potential to streamline production and increase competitiveness in the market. A critical element in the decision-making process of this agri-system is the weight gain of broiler chickens. Variability in chicken growth patterns can affect production plans in practice. The most efficient weight estimation models require sensors in real-time, which are not available in many companies. Thus, some small farms require a simpler approach to adjust production planning based on the uncertainty of chickens’ weight while they transition to new technologies. This research proposes an optimization-based methodology for the integration of farms and slaughterhouses to plan production under chicken growth uncertainty. The methodology includes two models: (i) a two-stage stochastic model that supports lot-sizing and inventory management decisions while considering scenarios of chicken growth uncertainty; and (ii) a mixed-integer linear programming model that supports lot allocation. We present the results of implementing the methodology in a poultry company in Santa Marta (Colombia), where we improved costs by 8.6% while meeting tactical, biological, and biosecurity constraints.

AN EFFECTIVE ACTION RECOGNITION APPROACH USED IN PHYSICAL EXERCISE FOR COPD PATIENTS

This study was conducted to evaluate the effect of computer vision-based respiratory rehabilitation. Chronic obstructive pulmonary disease (COPD) is one of the primary respiratory diseases worldwide. Recently, image-capturing devices are increasingly used for physical therapy during rehabilitation treatment. Among these technologies, Action recognition plays a critical role in physical exercise and rehabilitation evaluation. This study aimed to propose an action series of a respiratory training program consisting of six actions. A video camera was placed in front of the participants to record their movements. Then, a hybrid algorithm combined with a convolution neural network and long short-term memory models was employed for action recognition from a video recording. The results indicated that the model achieved a reliable classification level of 82.35% on six actions. This demonstrated the validity of the proposed approach for multi-category action recognition. It was effective for action evaluation without medical guidance under home-based rehabilitation. Furthermore, the model for weight estimation was light-weight, with no need to consider the processing time.

Weight estimation method of core components for more-electric aircraft based on component model

In the era of rapid development of avionics, the rise of More-Electric Aircraft (MEA) and All-Electric Aircraft (AEA) has become the necessity of the development of aviation aircraft. This paper presents a weight estimation method of core components of MEA based on the component model, which mainly solves the problems of limitations, complexity, and inaccurate estimation results of existing methods. The weight estimation method adopts the mathematical idea of regression analysis, establishes the linear and nonlinear regression models of aircraft component weight and design parameters, and obtains the weight estimation model with the design parameters of main components as independent variables, and the weight of core components as dependent variables, which is in line with the theory and practice. The results have strong reliability and can provide theoretical help for the later model design.