Mean Absolute Deviation Values Research Articles

Evaluation of Trend Models Performance and Forecasting Onion Production: A Comparative Study

Onions, a significant spice crop in Bangladesh, frequently experience price surges due to an imbalance between demand and supply, compelling the government to import onions. The volatility of the onion market in Bangladesh and the measures needed to stabilize the situation require intensive policy attention. Therefore, this study was carried out to forecast the onion production over time and to evaluate the efficacy of various trend models. The secondary data was collected from the Food and Agriculture Organization's Statistical Database (FAOSTAT) from 1972 to 2022. The autoregressive integrated moving average (ARIMA) model was used to predict the onion production over time. Additionally, metrics such as modified and the coefficient of determination were used to evaluate the effectiveness of eight trend models comprehensively. The findings indicate that the cubic model is superior to others since it has larger and adjusted values. The models' accuracy is also determined utilizing mean absolute percentage error (MAPE) and mean absolute deviation (MAD) metrics. The cubic trend model exhibits lower MAPE and MAD values of 29.43% and 80638.67, respectively. Therefore, it is concluded that the ARIMA (2, 2, 2) model is the best model for forecasting the annual production of onion in Bangladesh. A five-year trend analysis spanning from 2023-2027 indicates an upward trend. Such a result offers a critical insight for stakeholders and policymakers in formulating sustainable onion production strategies. J Bangladesh Agril Univ 22(3): 386-395, 2024

Entropy optimization on Casson nanofluid flow with radiation and Arrhenius activation energy over different geometries: A numerical and statistical approach

This study employs numerical and statistical approaches to investigate the entropy optimization of steady Casson nanofluid flow over three different geometries subject to boundary conditions induced by convective flow. Multiple linear regression is employed to statistically examine. The present model incorporates several novel elements, such as Arrhenius activation energy, Brownian motion, the Cattaneo-Christov dual flux, thermophoresis, thermal radiation, and so on. Moreover, a comparison is presented between Newtonian and non-Newtonian fluids. By applying the proper similarity transformations, ordinary differential equations (ODEs) are obtained by converting foundational partial differential equations (PDEs). The Runge-Kutta fourth-order method is utilised to solve the obtained ODEs along with the shooting technique. The outcomes are visually depicted via tables and graphs. The velocity drops with increasing Grashof number, and the magnetic field becomes progressively more forceful as the suction parameter increases. The temperature gets reduced with the increase of the suction parameter, solute Grashof number increases with the magnetic field, thermophoresis, and radiation parameters. The entropy is observed to rise with the increase of the effective parameters (magnetic field, Brinkmann number and radiation). The MAD (mean absolute deviation), MSE (mean squared error), and RMSE (root mean square error) values are approaching zero, indicating that the derived outcomes are highly accurate. A lower MAPE (mean absolute percentage error) suggests that the model has a higher level of precision. Therefore, the outcomes of the present model are more precise and reliable. This study has various potential applications such as power plant heat exchangers, material processing industries, and solar thermal energy systems.

Variational quantum algorithm for forecasting drugs for corrosion inhibitor

This study explores the development and evaluation of a Variational Quantum Algorithm (VQA) for predicting a drug as a corrosion inhibitor, highlighting its advantages over traditional regression models. The VQA leverages quantum-enhanced feature mapping and optimization techniques to capture complex, non-linear relationships within the data. Comparative analysis with AutoRegressive with exogenous inputs (ARX) and Gradient Boosting (GB) models demonstrate the superior performance of VQA across key metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Deviation (MAD). The VQA achieved the lowest RMSE (4.40), MAE (3.33), and MAD (3.17) values, indicating enhanced predictive accuracy and stability. These results underscore the potential of quantum machine learning techniques in advancing predictive modeling capabilities, offering significant improvements in accuracy and consistency over classical methods. The findings suggest that VQA is a promising approach for applications requiring high precision and reliability, paving the way for broader adoption of quantum-enhanced models in material science and beyond.

COMPARISON OF FORECASTING DRUG NEEDS USING TIME SERIES METHODS IN HEALTHCARE FACILITIES: A SYSTEMATIC REVIEW

Drug planning is essential to ensure the fulfillment of the right type, amount, and time criteria. Forecasting can be utilized during the planning stage to predict future drug needs. Perfect forecasting is impossible due to uncertainties in various factors, necessitating selecting the best method. This study aimed to identify the optimal forecasting method for healthcare facilities based on the smallest Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE) values obtained from forecasting results using time series methods like Single Moving Average (SMA), Weight Moving Average (WMA), (Single Exponential Smoothing) SES, Double Exponential Smoothing (DES), and Triple Exponential Smoothing (TES). This research involved a descriptive observational study with retrospective data and adhered to PRISMA guidelines. PubMed, Google Scholar, and Garuda served as the data sources. Nine articles meeting the eligibility criteria were employed. The findings revealed that the SES, DES, and TES methods produced forecasts with MAPE values below 10%, indicating highly accurate forecasting. The MAPE values for the SMA and WMA methods were less than 50%, which is still acceptable. Therefore, the ES methods, particularly SES, are highly recommended for accurate drug planning. Forecasting accuracy factors include data stability, pattern consistency, and smoothing constants. The SES method emerged as the best forecasting method, generating the smallest MAD, MSE, and MAPE values compared to other methods, falling below 10%, reflecting highly accurate forecasting.

Optimisation of Inventory Management Through Time Series Analysis of Inventory Data with Double Exponential Smoothing Method

Stock forecasting is very useful for companies in knowing the trend of inventory needed in the next period, with time series data often forecasting can be a solution in supporting decision making. Excess or lack of stock of goods is often caused by a less than optimal record management process and often relies on personal intuition. In this study, the Double Exponential Smoothing method is applied in analyzing time series data and forecasting stock data. This method is used because it is in accordance with the company's sales data which is up and down. In addition, this forecasting calculation does not escape the error rate of forecasting calculations, therefore this system is also supported by the MAD (Mean Absolute Deviation), MSE (Mean Square Error) and MAPE (Mean Absolute Percentage) methods to calculate the error rate of the forecasting results. The forecasting results show that this method is able to provide fairly accurate predictions with a MAD value of 5.2475, MSE of 43.009, and MAPE of 26.307%. By using DES, companies can perform better stock planning, reduce the risk of over- or under-stocking, and improve inventory management efficiency. The DES method is proven to be flexible and easy to implement in computerized information systems, so it is recommended to be used more widely in corporate inventory management.

Prediksi Harga Material Bangunan Dengan Autoregressive Integrated Moving Average (Arima) Pada CV. TJA

The problem most often faced by CV TJA is that the price from the RAB (Cost Budget Plan) calculation is not in accordance with market prices, so it is necessary to predict the price of building materials to help the company prepare the RAB. The aim of this research is to examine predictions of material prices measured using the ARIMA method. The data analysis method in this study used the MA parameter (1) with the ARIMA model (0,1,1) for dynamix cement material, the MA parameter (1) ARIMA model (0,2,1) for cast sand, the MA parameter (1 ) ARIMA model (0,3,1) for threaded iron 13 and AR parameter (2) ARIMA model (2,1,0) for usuk wood material. By using 3 error testing methods, namely Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Squared Deviation (MSD). The results of this analysis show the lowest accuracy value, namely for dynamic cement material with a MAPE value of 5%, a MAD value of 1.986 and an MSD value is 63.584.667. The results of error tests using MAPE, MAD and MSD can be concluded that the ARIMA method is very accurate because the MAPE value is less than 10%.

The M-Estimator and S-Estimator in Robust Improved Geographically and Temporally Weighted Regression for Modelling GRDP in West Java, Indonesia

The success of development in a region in Indonesia can be measured by economic growth, especially in the financial sector using the Gross Regional Domestic Product (GRDP) growth rate. The GRDP figure at the Regency and City level in West Java is one of Indonesia's highest and most diverse. This is due to various factors, including the geographical location of West Java, which is directly adjacent to DKI Jakarta, which is the center of the national economy. Although classified as high, the diversity of GRDP values between regions in West Java needs attention to equalize economic growth. The diversity of GRDP values can be modeled by the Improved Geographically and Temporally Weighted Regression (I-GTWR) method by taking samples in 2018-2022. The I-GTWR modeling method considers the influence of spatial heterogeneity and spatial-temporal interaction, which has been proven to produce better results than the GTWR method in modeling GRDP in Central Java in 2011-2015. This study also adds M-estimators and S-estimators to improve the model's performance and make it robust to outliers. The explanatory variables we use are Regional Original Revenue, General Allocation Fund, Foreign Investment, Regional Minimum Wage, Domestic Investment, Poverty, Per-Capita Expenditure, and Number of Job Vacancies. The analysis shows that the Robust I-GTWR model, especially the M-estimator, produces better model performance than the I-GTWR model in modeling West Java GRDP. The coefficient of determination made by the Robust I-GTWR method using the M-estimator is 94.62% with a mean absolute deviation value of 0.1491 and an Akaike Information Criterion value of 110.4899.

Global mapping of fractional tree cover for forest cover change analysis

Fractional tree cover facilitates the characterization of forest cover changes using satellite data. However, there are still substantial challenges in generating fractional tree cover datasets that satisfy the requirements of interannual stability for forest cover change monitoring. In this study, a global annual fractional tree cover dataset, named as GLOBMAP Fractional Tree Cover, was generated from MODIS observations with a resolution of 250 m during the period of 2000–2022. The tree cover estimation was improved relative to conventional global tree cover mapping methods by developing highly discriminative input features and near global sampling training data. MODIS annual observations were realigned at the pixel level to eliminate phenological differences among regions. The realigned annual series were condensed into twelve features showing high separability between trees and herbaceous vegetation to reduce the dimension of features. A global covering training dataset, comprising 465.88 million sample points, was extracted across the globe through the aggregation and combination of forest/land cover maps from ESA WorldCover, GlobeLand30, PALSAR FNF, and ESRI Land Cover to improve the representativeness of training data. A feed-forward neural network was calibrated to predict fractional tree cover from MODIS data. The spatial pattern of the estimation results was generally consistent with the CGLS-LC100 product at global scale, with the average MAE in global vegetated areas of 12.50 %, while our dataset provided extended temporal coverage. The interannual stability of the estimated tree cover series was improved compared to MODIS vegetation continuous fields products for deciduous broadleaf forests, evergreen broadleaf forests, and mixed forests, with the global average value of mean absolute deviation (MAD) in tree cover series reduced by 5.8 %, 46.9 %, and 18.3 %, respectively. The estimation results were assessed using globally distributed validation data around BELMANIP 2.1 sites and those derived from the USGS circa 2010 global land cover reference dataset, leading to the R2 values of 0.93 and 0.73, MAE of 4.24 % and 10.55 %, and RMSE of 11.45 % and 17.98 %, respectively. This dataset could enhance the capability to monitor forest cover changes, particularly gradual changes such as forest recovery.

Using Machine Learning Techniques to Forecast Mehram Company's Sales: A Case Study

Sales forecasting, situated at the intersection of art and science, is critical for inspiring managers toward achieving profitable outcomes. Its precision sustains production levels and capital and plays a pivotal role in the company's and its leaders' overall success and career progression. In the context of Mahram Food Industries, the challenge arises from diverse investor perspectives and the impactful nature of numerous variables. To address this, a new sales forecasting algorithm has been introduced to enhance accuracy. The aim is to predict future sales through a comprehensive approach, leveraging technical analysis, time series modeling, machine learning, neural networks, and random forest techniques. The research methodology integrates various advanced techniques to improve sales forecasting for Mahram Food Industries. Technical analysis, time series modeling, machine learning, neural networks, and random forest methods are combined to create a robust framework. The focus is on predicting sales for a future period within the artificial intelligence-based machine learning domain. The study employs metrics such as Mean Absolute Deviation (MAD), MAD Percentage (MADP), and Mean Squared Error (MSE) to evaluate and compare the performance of the proposed neural network against traditional methods like multiple variable regression and time series modeling. The study's results highlight the superior performance of the neural network in sales forecasting for Mahram Food Industries. The Mean Absolute Deviation (MAD) for the neural network is 28.33, outperforming multiple variable regression (28.54) and time series modeling (29.45). Additionally, the neural network demonstrates a better MAD Percentage (MADP) with a value of 10.2%, surpassing the values associated with multiple variable regression (10.35%) and time series modeling (10.30%). The Mean Squared Error (MSE) further confirms the neural network's superiority with a value of 6452 compared to 6472 and 7865 for multiple variable regression and time series modeling, respectively. In conclusion, the study showcases the effectiveness of integrating advanced techniques, particularly the neural network, in enhancing the accuracy of sales forecasting for Mahram Food Industries. The comprehensive approach, combining technical analysis, time series modeling, machine learning, neural networks, and random forest, is a valuable strategy for predicting future sales. The superior performance of the neural network, as evidenced by lower MAD, MADP, and MSE values, suggests its potential for guiding informed decision-making in goal setting, hiring, budgeting, and other critical aspects of business management.

PENGGUNAAN PROYEKSI TREN DI UMKM NN UNTUK MEMENUHI PERMINTAAN KONSUMEN

MSMEs (Micro, Small and Medium Enterprises) in Indonesia have a major influence in terms of economic growth. NN MSMEs are MSMEs engaged in culinary. One type of NN MSME product is ready-to-eat emping. Often during the Eid al-Fitr holiday experience overwhelmed in terms of meeting the demand for ready-to-eat emping. This study aims to assist NN MSMEs in estimating the amount of consumer demand for ready-to-eat emping, as well as to find out the best results and methods in calculating forecasting in trend projection methods. The trend projection method used in forecasting calculations in this case is linear regression and least squares. Meanwhile, to calculate the error value from the forecasting results using the Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) methods. The best method from the results of this study is to use a linear regression method with demand values for 2024, 2025 and 2026 during Eid al-Fitr are 1771.23 kg, 1914.24 kg, and 2057.25 kg respectively with error values of MAD, MSE and MAPE respectively are 612.0781, 525807.4 and 48.83%.
 
 Keywords: Forecasting, Trend Projections, MSMEs, Consumer Demand

Analisis Peramalan Kebutuhan Komponen Minifix Bolt Dia di PT XYZ Menggunakan Metode Time Series

PT XYZ is a company that produces furniture products that use the "Make to stock" method to meet consumer demand. In the production process, there is often a buildup of raw materials, one of which is the minifix bolt component, which causes a buildup of raw materials in the warehouse. Therefore, it is necessary to forecast raw materials to predict the amount of raw materials each period. In the forecasting process, you can use the moving average and single ex-ponential smoothing methods. From the calculation results using the POM-QM software, the MAD (Mean Absolute Deviation) value was 13,111.11, MSE (Mean Squared Error) was 261,975,300, MAPE (Mean Absolute Percent Error) was 226,303%. From the software output results, it is known that the smallest MAD, MSE, and MAPE values ​​of the three methods are the moving average method. So this method was chosen to solve problems within the company because the smaller the error that occurs, the smaller the possibility of something bad happening.

Forecasting COVID-19 Daily Infected Cases in Sri Lanka by Holt-Winters Exponential Smoothing Method

The novel coronavirus disease (COVID-19) has spread from China since December 2019 and spread worldwide including Sri Lanka. The aim of this study was to forecast the daily infected cases of COVID-19 in Sri Lanka which in turn help administrators for effective management of the pandemic. The method used in this study was Holt-Winters three parameter with additive or multiplicative models. The daily infected cases in Sri Lanka during the period of 22nd January 2020 to 22nd December 2021 were obtained from the publicly available databases of Epidemiology Unit of Sri Lanka and World Health Organization. The pattern recognition of the daily infected cases was examined by time series plot and Auto Correlation Function (ACF). The model validation was performed by the Anderson Darling test which confirmed the normality of residuals (p > 0.05) and ACF that confirmed the independence of residuals of the model. The forecasting ability of the model was assessed by the three measurements of errors; Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Square Error (MSE). Holt-Winters additive and multiplicative model with α (level) 0.61, β (trend) 0.4 and γ (seasonal) 0.3 at a length of repeating behaviour of 3 days, had the least relative and absolute measurement of errors during the model fitting and verification. İn the multiplicative model, MAPE, MAD and MSE were 0.2847, 0.0187 and 0.0005 respectively. Similarly in the additive model, corresponding values of MAPE, MAD and MSE were 0.0207, 0.0187 and 0.0005. The fits and the forecast of these models followed a similar pattern of the actual daily infected cases concluding that the Holt-Winters model can be used to forecast the COVID-19 outbreak in Sri Lanka.

Employing RSM and ANN-based applications for modelling enhanced bacterial cellulose production from pineapple peel waste using Komagateibacter saccharivorans APPK1

Bacterial cellulose (BC) is a bio-polymer with variety of applications in diverse fields such as biomedical, cosmetics, environment and food. Owing to its superior crystallinity, purity and other unique properties BC outshines plant cellulose in several aspects. The current study focuses on a cost-effective method for BC production from pineapple waste using Komagateibacter saccharivorans APPK1. The research includes a systematic optimisation approach that includes screening of various components in the medium. The variables under investigation in this study were ammonium sulphate, ethanol volume and incubation days. These variables were selected and evaluated using the Plackett-Burman experimental design model. Additionally, the engineering of process parameters using a process-driven approach i.e., response surface methodology (RSM) and a data-driven approach i.e., artificial neural network (ANN) were employed. BC production was increased up to 43 % with a maximum yield of 47.5 g/100 mL using RSM. However, on comparing RSM and ANN models for enhanced BC production based on error functions such as R2 value, mean absolute percentage error, root mean square error, and mean absolute deviation values, the ANN model emerged as the superior one. As this is the first report to the authors’ best knowledge on model development using a hybrid approach of RSM and ANN for enhanced BC production using Komagateibacter saccharivorans APPK1, this could lead to more in-depth and large-scale production of BC.

Emulation of seismic-phase traveltimes with machine learning

SUMMARY We present a machine learning (ML) method for emulating seismic-phase traveltimes that are computed using a global-scale 3-D earth model and physics-based ray tracing. Accurate traveltime predictions based on 3-D earth models are known to reduce the bias of event location estimates, increase our ability to assign phase labels to seismic detections and associate detections to events. However, practical use of 3-D models is challenged by slow computational speed and the unwieldiness of pre-computed lookup tables that are often large and have prescribed computational grids. In this work, we train a ML emulator using pre-computed traveltimes, resulting in a compact and computationally fast way to approximate traveltimes that are based on a 3-D earth model. Our model is trained using approximately 850 million P-wave traveltimes that are based on the global LLNL-G3D-JPS model, which was developed for more accurate event location. The training-set consists of traveltimes between 10 393 global seismic stations and randomly sampled event locations that provide a prescribed, distance-dependent geographic sample density for each station. Prediction accuracy is dependent on event-station distance and whether the station was included in the training set. For stations included in the training set the mean absolute deviation (MAD) of the difference between traveltimes computed using ray tracing through the 3-D model and the ML emulator for local, regional, and teleseismic distances are 0.090, 0.125 and 0.121 s, respectively. For tested station locations not included in the training set, MAD values for the three distance ranges increase to 0.173, 0.219 and 0.210 s, respectively. Empirical traveltime residuals for a global reference data are indistinguishable when ML emulation or the 3-D model is used to compute traveltimes. This result holds regardless of whether the recording station is used in ML training or not.

Prediction of inhibition performance of agro-waste extract in simulated acidizing media via machine learning

The study aims to employ machine learning modelling approach to model the measurement of corrosion rate on AISI 316 stainless steel when corrosion inhibitor is added in different dosages and dose schedules. To achieve this, experimental data was analyzed statistically and modeled using Levenberg-Marquardt’s back-propagation artificial neural network (LMBP-ANN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms. Maximum inhibition efficiencies of 96.44%, 94.74%, and 90.24% were obtained from experimental at a concentration of 10 g and temperatures of 288, 298, and 308 K respectively. The experiment shows that the corrosion rate time profile depends on the dosing schedule, whereas the final rate mainly depends on the environmental severity. The corrosion rates are predicted by the developed models while their capabilities were compared in terms of Mean Absolute Percentage Error root (MAPE), determination coefficient (R2), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE) for all outputs. From the statistical metrics obtained, credence was given to ANFIS as the best predictive model compared to the LMBP-ANN with MAPE, R2, MAD, and RMSE value of 15.242,0.893, 0.105,0.372 for corrosion rate, 13.135,0.904, 0.725,1.036, for weight loss and 18.342, 0.835, 20.417, 24.238 for inhibition efficiency at the testing stage. The effect of inhibitor concentration and exposure time are the most significant parameters for predicting eggshell extract as potential inhibitor for stainless steel in oilfield pickling and acidizing media.

ChatGPT: unlocking the potential of Artifical Intelligence in COVID-19 monitoring and prediction.

The COVID-19 pandemic has had an unprecedent impact of everyday life with deleterious consequences on global health, economics, and society. Thus, accurate and timely information is critical for monitoring its spread and mitigating its impact. ChatGPT is a large language model chatbot with artificial intelligence, developed by OpenAI, that can provide both textual content and R code for predictive models. It may prove to be useful in analyzing and interpreting COVID-19-related data. This paper explores the application of ChatGPT to the monitoring of the COVID-19 pandemic, presenting R code for predictive models and demonstrating the model's capabilities in sentiment analysis, information extraction, and predictive modelling. We used the prediction models suggested by ChatGPT to predict the daily number of COVID-19 deaths in Italy. The prediction accuracy of the models was compared using the following metrics: mean squared error (MSE), mean absolute deviation (MAD) and root mean squared error (RMSE). ChatGPT suggested three different predictive models, including ARIMA, Random Forest and Prophet. The ARIMA model outperformed the other two models in predicting the daily number of COVID-19 deaths in Italy, with lower MSE, MAD, and RMSE values as compared to the Random Forest and Prophet. This paper demonstrates the potential of ChatGPT as a valuable tool in the monitoring of the pandemic. By processing large amounts of data and providing relevant information, ChatGPT has the potential to provide accurate and timely insights, and support decision-making processes to mitigate the spread and impact of pandemics. The paper highlights the importance of exploring the capabilities of artificial intelligence in the management of public emergencies and provides a starting point for future research in this area.

Optimizing anaerobic co-digestion of Xyris capensis and duck waste using neuro-fuzzy model and response surface methodology

Optimization of biomethane yield from the anaerobic co-digestion process has taken a new dimension from the application of common statistical techniques to the use of intelligence models in the Artificial Intelligence (AI) field. In this study, the performance of fuzzy c-means (FCM)-clustered adaptive neuro-fuzzy inference system (ANFIS) was compared with the Response Surface Methodology (RSM) in investigating the impact of critical process parameters, namely temperature, retention time, and mixing ratio on the methane yield of anaerobic co-digestion of duck waste and Xyris capensis. Relevant statistical metrics such as root mean square error (RMSE), mean absolute percent error (MAPE), mean absolute error (MAE) and mean absolute deviation (MAD) was used to evaluate the performance of the FCM-ANFIS model developed. The developed RSM and FCM-ANFIS models were compared based on the predicted yields and optimum conditions. It was observed from the results that the process parameters have a significant influence on the methane yield of anaerobic co-digestion of duck waste and Xyris capensis. The cumulative yield of 478.42, 478.43, and 436.20 mL CH4/g VSadded was observed for experimental, RSM, and ANFIS-FCM5clusters models, respectively. The optimum daily methane yield was observed when the mixing ratio was 75% duck waste and 25% Xyris capensis for the experimental and ANFIS-FCM5 clusters model. In contrast, the RSM model observed it at 100% duck waste. The RMSE, MAD, MAE, and MAPE values of the optimal neuro-fuzzy model (ANFIS-FCM5clusters) test indicated that the neuro-fuzzy model developed performs better than the RSM model with minimal error and higher accuracy. RSM and ANFIS models were noticed to be practical models for methane yield optimization and prediction from anaerobic co-digestion of livestock waste and lignocellulose feedstocks in a multiple-input variable without undertaking an experiment within a short time and error. These models can be used to optimize the anaerobic digestion process of lignocellulose materials and livestock waste at the industrial scale.

Three-Dimensional Measurement of Full Profile of Steel Rail Cross-Section Based on Line-Structured Light

The wear condition of steel rails directly affects the safety of railway operations. Line-structured-light visual measurement technology is used for online measurement of rail wear due to its ability to achieve high-precision dynamic measurements. However, in dynamic measurements, the random deviation of the measurement plane caused by the vibration of the railcar results in changes in the actual measured rail profile relative to its cross-sectional profile, ultimately leading to measurement deviations. To address these issues, this paper proposes a method for three-dimensional measurement of steel rail cross-sectional profiles based on binocular line-structured light. Firstly, calibrated dual cameras are used to simultaneously capture the profiles of both sides of the steel rail in the same world coordinate system, forming the complete rail profile. Then, considering that the wear at the rail waist is zero in actual operation, the coordinate of the circle center on both sides of the rail waist are connected to form feature vectors. The measured steel rail profile is aligned with the corresponding feature vectors of the standard steel rail model to achieve initial registration; next, the rail profile that has completed the preliminary matching is accurately matched with the target model based on the iterative closest point (ICP) algorithm. Finally, by comparing the projected complete rail profile onto the rail cross-sectional plane with the standard 3D rail model, the amount of wear on the railhead can be obtained. The experimental results indicate that the proposed line-structured-light measurement method for the complete rail profile, when compared to the measurements obtained from the rail wear gauge, exhibits smaller mean absolute deviation (MAD) and root mean square error (RMSE) for both the vertical and lateral dimensions. The MAD values for the vertical and lateral measurements are 0.009 mm and 0.039 mm, respectively, while the RMSE values are 0.011 mm and 0.048 mm. The MAD and RMSE values for the vertical and lateral wear measurements are lower than those obtained using the standard two-dimensional rail profile measurement method. Furthermore, it effectively eliminates the impact of vibrations during the dynamic measurement process, showcasing its practical engineering application value.

Correlation and determination of the Metabolizable Energy (ME) of tropical forage with nutrient content for ruminants

The metabolizable energy (ME) of tropical forages measured by in vivo method in ruminants had a high degree of accuracy but requires a long time and is expensive. One method that can be done is the ME estimation model. The objectives of the present study were carried out to investigate the relationship between tropical forage nutrient content and ME for ruminants as well as determine and validate a model for estimating ME of tropical forage based on nutrient content. A total of 26 forage samples consisting of 14 types of grass and 12 legumes were obtained after data pre-processing or data cleaning and data normalization. Forage samples will be grouped into 3, Grass + Legume (G+L=26), grass (R=14), and legume (L=12). The database used is Crude Protein (CP), Extract Ether (EE), Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), and hemicellulose as well as ME with in vivo experiments. The initial stage is preprocessing data. Nutrient content and ME were analyzed using Pearson Correlation and followed by multiple linear regression to determine the ME estimation model. However, validated used the mean absolute deviation (MAD), root means square error (RMSE), and mean absolute percentage error (MAPE). The results showed that there was a significant and highly significantly correlated between nutrient composition and ME in the Grass + Legume, Grass, and Legume groups so it could be used to determine ME. There are 9 regression equations with significance and have high R2 and after being validated with the lowest MAD, RMSE, and MAPE values, three regression equations are obtained with one each for each group Grass + Legume (G+L), Grass (R), and Legumes (L). It is concluded that the regression equation of ME of tropical forage is MER+L = 12.429 – 0.122 ADF for Grass + Legume, EMR = 15.609 – 0.115 NDF for Grass, and EML = 3.726 – 0.186 CP for Legume.

Proposal of a non-linear model to adjust in vitro gas production at different incubation times

This work aims to propose a new model named Gompertz-Von Bertalanffy bicompartmental (GVB), a combination of the models Gompertz and Von Bertalanffy. The GVB models is applied to fit the kinetic curve of cumulative gas production (CGP) of four foods (SS – sunflower silage; CS – corn silage; and the mixtures 340SS – 660 gkg-1 of corn silage and 340 gkg-1 of sunflower silage; and 660SS – 340 gkg-1 of corn silage and 660 gkg-1 of sunflower silage). The GVB fit is compared to models Logistic-Von Bertalanffy bicompartmental (LVB) and bicompartmental logistic (BL). All the process studied employed the semi-automatic “in vitro” technique of producing gases used in ruminant nutrition. The gas production readout was performed at times 2, 4, 6, 8, 10, 12, 15, 19, 24, 30, 48, 72, and 96 h. The data generated were used to estimate the models’ parameters by the least squared method with the iterative Gauss-Newton process. The data fit quality of the models was verified using the adjusted coefficient of determination criterion (), mean residual square (MRS), Akaike information criterion (AIC), and mean absolute deviation (MAD). Among the analyzed models, the LVB model presented the best quality of fit evaluators for CS. In contrast, the GVB model showed better quality of fit to describe CGP over time for 340SS, 660SS, and SS, presenting the highest values of () and the lowest values of MSR, AIC, and MAD.