Prediction Equations Research Articles

Numerical Simulation of Subaerial Granular Landslide Impulse Waves and Their Behaviour on a Slope Using a Coupled Smoothed Particle Hydrodynamics–Discrete Element Method

Numerical simulations were conducted to investigate the wave features of subaerial granular landslide-generated impulse waves and their impact on slopes. A numerical solution was obtained by coupling smoothed particle hydrodynamics (SPH) and the discrete element method (DEM). Several predictive equations were tested for their applicability in predicting the maximum crest amplitude of impulse waves generated by slides of different shapes. The results indicated that the predictive model developed by Heller and Hager, utilising slide centroid impact velocity, showed favourable prediction accuracy for the maximum crest amplitude, almost independent of the slide shape at impact. Regarding the leading wave, although the wave profile and velocity distribution deviated significantly from a solitary wave of the same wave amplitude, the maximum run-up could be satisfactorily estimated using solitary wave theory. In addition, the increase in the maximum dynamic forces exerted by the impulse waves on the slope followed a power law with the incident wave amplitude.

Dependence of horizontal PGA prediction on site conditions at critically short hypocentral distances: an analysis based on Japanese strong motion data

A fundamental element in seismic hazard assessment studies is the prediction of maximum values of ground accelerations. This is achieved through an attenuation relationship, also known as the ground motion prediction equation (GMPE). Seismic design of structures generally assumes the occurrence of a large earthquake on a nearby fault. The predictions from the GMPE are linked to the magnitude, distance, and local site conditions. Critical near‑fault ground motions have unique characteristics such as magnitude saturation and distance saturation. To investigate the impact of local site conditions in the near‑fault region, we analyzed the attenuation relations using the accelerations recorded in Japan by the Kiban Kyoshin network (KiK‑net) from 2000 to 2023. All events with a magnitude equal to or greater than 5.5, and with focal depth and epicentral distance less than 20 km, were considered. In this study, the hypocentral distance ranges from 5 to 23 km. Ordinary least squares regression analysis was conducted for the maximum horizontal vector of ground accelerations. The attenuation relation coefficients for horizontal vector peak ground acceleration were estimated using an effective distance and saturation effect. An additional independent term, the average shear wave velocity of the soil to a depth of 30 meters below the ground surface, and more cost‑effective shallower depths were included in the regression model. The analysis revealed that the site condition is statistically non‑significant at very short hypocentral distances of less than 23 kilometers.

Factors Affecting the Adjustment of Pre-Service Teachers, Faculty of Education, Buriram Rajabhat University

This research aimed to 1) study the relationship of factors affecting the adjustment of pre-service teachers in the Faculty of Education, Buriram Rajabhat University, and 2) study the factors affecting the adjustment of pre-service teachers in the Faculty of Education, Buriram Rajabhat University. The sample group consisted of 242 pre-service teachers from the Faculty of Education, Buriram Rajabhat University, selected by simple random sampling. The data collection tool was a 5-point Likert scale questionnaire. The statistics used for data analysis included mean, percentage, standard deviation, Pearson's correlation coefficient, and multiple linear regression analysis using the Stepwise method. Research findings revealed that 1) the relationship between the factors affecting the adjustment of pre-service teachers in the Faculty of Education, Buriram Rajabhat University had correlation coefficients ranging from 0.442 to 0.703, with all aspects showing statistical significance at the .01 level. The factors most related to adjustment were achievement motivation, followed by peer relationships, and attitude towards learning, respectively, and 2) the factors affecting the adjustment of pre-service teachers in the Faculty of Education that could predict their adjustment were: achievement motivation (β = 0.496, b = .525), peer relationships (β = 0.238, b = .221), and the relationship between mentor teachers and students (β = 0.139, b = .118). These factors could predict the adjustment of pre-service teachers in the Faculty of Education, Buriram Rajabhat University, by 55.60% (R2 = .556, p < .001). The predictive equation in unstandardized scores is Y = 0.461 + 0.525X2 + 0.221X4 + 0.118X5, and the predictive equation in standardized scores is ZY = 0.496ZX2 + 0.238ZX4 + 0.139ZX5.

Relationship between health belief levels and fear of cancer recurrence in breast cancer patients: Development of a predictive model

This study examines the relationship between health beliefs and fear of cancer recurrence among breast cancer (BC) patients and develops a predictive model for cancer recurrence fear. A total of 120 patients from the Breast Surgery Department of a top tertiary hospital, spanning December 2022 to December 2023, were included in the study. Data were collected using general information questionnaires, Champion’s Health Belief Model Scale (CHBMS) and the Fear of Progression Questionnaire-Short Form (FoP-Q-SF). The analysis revealed that CHBMS and FoP-Q-SF scale scores of BC patients were 94.17 ± 15.59 points and 34.90 ± 7.15 points, respectively. The total CHBMS score, perceived benefits, health motivation, and self-efficacy were negatively correlated with the total FoP-Q-SF score and its dimension scores (P < 0.05), whereas perceived severity, perceived susceptibility, type of operation, radiotherapy and chemotherapy treatment, and perceived barriers showed a positive correlation (P < 0.05). Significant differences in FoP-Q-SF scores were observed among patients of different ages, marital statuses, education levels, average monthly household incomes, levels of family caregiving, tumor node metastasis (TNM) stages, and types of medical insurance (P < 0.05). Multivariate logistic regression analysis identified age, marital status, education level, average monthly household income, family caregiving, TNM stage, chemoradiotherapy treatment, and CHBMS scale scores as significant factors influencing cancer recurrence fear (P < 0.05). A predictive model regression equation was established, and the model’s area under the ROC curve was 0.759 (95% confidence interval = 0.702 – 0.834, P < 0.001). Patients aged 50 years and older exhibited higher fear levels. This fear is also affected by age, marital status, education level, average monthly household income, family caregiving, TNM stage, and chemoradiotherapy treatment. The predictive model based on these factors demonstrates good predictive efficacy for cancer recurrence fear, providing a basis for early identification of high-risk patients and targeted nursing interventions.

Calculated estimation of the metabolic clearance rate of insulin measured by glucose clamp examination in out-patient clinical practice.

A subpopulation of Japanese patients with type 2 diabetes mellitus (T2DM) who have elevated insulin clearance (IC) exists. We tested our hypothesis that it is possible to estimate IC using common and simple test results collected in routine clinical practice. We recruited patients with newly diagnosed, treatment-naïve T2DM and measured the metabolic clearance rate of insulin (MCRI) determined by a hyperinsulinemic-euglycemic clamp examination. Multivariable regression analysis was performed with body mass index (BMI), serum uric acid (UA), and fasting plasma insulin (F-IRI) which were independently associated with IC increase in our previous reports as explanatory variables to calculate a prediction equation for MCRI. We enrolled 101 patients in this study. Because MCRI is not normally distributed, we calculated the logarithmically transformed estimated Log10MCRI as a prediction formula for IC. Multivariable regression analysis showed that Log10BMI (β = - 0.3257, P < 0.001), UA (β = - 0.1834, P = 0.0081), and Log10F-IRI (β = - 0.4367, P < 0.001) were significant independent factors for Log10MCRI. The regression equation was as follows: estimated Log10MCRI = - 0.5421 × Log10BMI - 0.0167 × UA - 0.1792 × Log10F-IRI + 3.8251 (r = 0.7677, R 2 = 0.5894, P < 0.001). IC can easily be predicted using BMI, UA, and F-IRI which are common and simple test results collected in routine clinical practice.

Anthropometric prediction models of body composition in 3 to 24month old infants: a multicenter international study

BackgroundAccurate assessment of body composition during infancy is an important marker of early growth. This study aimed to develop anthropometric models to predict body composition in 3–24-month-old infants from diverse socioeconomic settings and ethnic groups.MethodsAn observational, longitudinal, prospective, multi-country study of infants from 3 to 24 months with body composition assessed at three monthly intervals using deuterium dilution (DD) and anthropometry. Linear mixed modelling was utilized to generate sex-specific fat mass (FM) and fat-free mass (FFM) prediction equations, using length(m), weight-for-length (kg/m), triceps and subscapular skinfolds and South Asian ethnicity as variables. The study sample consisted of 1896 (942 measurements from 310 girls) training data sets, 941 (441 measurements from 154 girls) validation data sets of 3–24 months from Brazil, Pakistan, South Africa and Sri Lanka. The external validation group (test) comprised 349 measurements from 250 (185 from 124 girls) infants 3–6 months of age from South Africa, Australia and India.ResultsSex-specific equations for three age categories (3–9 months; 10–18 months; 19–24 months) were developed, validated on same population and externally validated. Root mean squared error (RMSE) was similar between training, validation and test data for assessment of FM and FFM in boys and in girls. RMSPE and mean absolute percentage error (MAPE) were higher in validation compared to test data for predicting FM, however, in the assessment of FFM, both measures were lower in validation data. RMSE for test data from South Africa (M/F−0.46/0.45 kg) showed good agreement with validation data for assessment of FFM compared to Australia (M/F−0.51/0.33 kg) and India(M/F−0.77/0.80 kg).ConclusionsAnthropometry-based FFM prediction equations provide acceptable results. Assessments based on equations developed on similar populations are more applicable than those developed from a different population.

Neural-Network and Multivariate-Normal-Distribution Hybrid Method for Real-Time Ground-Shaking Reconstruction

ABSTRACT Ground-shaking maps provide a spatial representation of the impact of a seismic event in terms of ground-motion parameters (GMPs), especially useful in the context of seismic monitoring and civil protection operations. Algorithms used to compute these maps usually rely on seismic source parameters to steer the interpolation process and consequently are limited to operate in near-real time. The present work introduces a novel algorithm that combines neural networks with the multivariate normal distribution method to reconstruct ground-shaking maps using only data available in real time, improving on previously proposed algorithms. The core idea of the proposed algorithm is to maintain the structure proposed by ShakeMap while removing the dependence on the source parameters, imposed by the use of ground-motion prediction equations, by replacing them with an appropriate neural network working on the GMPs recorded in real time at the seismic stations. The overall workflow of the method and the details of the neural network architecture and training are described. A model trained on synthetic and recorded data to target seismic events affecting the Italian territory is tested using the 2016 Norcia, Italy, earthquake showing the method reconstruction capabilities, its robustness to noise and to network geometry changes, and its real-time potential.

Development of prediction equations for apparent metabolizable energy corrected for nitrogen of fish meal and poultry by-product meal in broiler chickens

Development of prediction equations for apparent metabolizable energy corrected for nitrogen of fish meal and poultry by-product meal in broiler chickens

Population reference equations for handheld peak expiratory flow in older U.S. adults

BackgroundPeak expiratory flow (PEF) is a simple, inexpensive measure of respiratory effort and is a valuable predictor of health outcomes in older adults. Yet, there is a lack of epidemiological data validating PEF prediction equations among older adult populations, especially those ≥80 years. The National Health and Aging Trends Study (NHATS) is a large, nationally representative sample of U.S. adults ages ≥65 years that offers a unique opportunity to develop PEF population reference equations. MethodsUsing a healthy subsample from the NHATS 2011 cohort (N = 1740; 68.9 % female; mean [SD] age = 77.0 [7.9] years), sex-specific reference equations were generated for PEF, measured via a handheld flow meter, using height and age as predictors. Reference equations for both sexes were validated against the NHATS 2015 cohort by testing measured vs. predicted PEF values. Additionally, new reference equations were compared to spirometry PEF reference equations from the National Health and Nutrition Examination Survey (NHANES). ResultsAfter applying NHATS 2011 reference equations to the NHATS 2015 cohort, measured vs. predicted PEF values were not significantly different (Ps > 0.05). The NHANES equations tended to slightly overestimate handheld PEF measurements in the NHATS 2015 cohort by an average of 29.3 L/min and 10.1 L/min in males and females, respectively. ConclusionsResults demonstrate the comparability of PEF reference equations from a handheld meter to spirometry reference equations in older adults. New reference equations can be applied to a traditionally undersampled population with an easily obtained and low-cost measure.

Prediction of strength activity index using chemical and physical properties of pozzolans

Reductions in cement use have essential benefits in reducing the embodied energy in concrete and CO2 emissions. Hence, effective assessment of potential pozzolanic materials is highly desirable to facilitate usage as sustainable supplementary cementitious materials (SCMs). However, assessment of pozzolanic reactivity using conventional experimental tests is typically time-consuming and expensive. Pozzolanic reactivity is mainly related to the chemical and physical characteristics of various pozzolans, such as amorphous silica and alumina contents and specific surface area. This study develops and presents an equation that can predict the strength activity index (SAI) as an indirect method for the assessment of potential pozzolans and their strength outcome using their chemical and physical properties. The development of a prediction equation not only saves time and resources but also helps with designing optimized and improved pozzolanic SCMs. The strength activity index (SAI) of seven different materials with varying pozzolanic properties was measured at an age of 90 days. The powdered test materials included pottery cull, brick powder, lightweight aggregate fines, glass powder, silica fume, dolostone, and Class C fly ash. In the second stage, correlation analyses were performed to find parameters (based on chemical and physical properties) that were highly correlated with SAI. An equation was then developed as a function of the chemical and physical properties of raw pozzolanic materials using an optimization tool. Consequently, an equation predicting SAI was derived which had a high degree of correlation (R = 0.972) with measured SAI.

Deep-neural-network model for predicting ground motion parameters using earthquake horizontal-to-vertical spectral ratios

This study proposed a deep-neural-network (DNN) model for seismic ground motion prediction by utilizing a unified strong motion database by the National Research Institute for Earth Science and Disaster Resilience, and earthquake horizontal-to-vertical spectral ratio (EHVR) database in Japan. The model aims to enhance the accuracy of predictions by incorporating the EHVRs for complementing site effects, and utilizing existing ground motion prediction equations (GMPE) as the base model for source and propagation path effects. The hybrid approach enables the prediction of peak ground accelerations (PGAs), peak ground velocities (PGVs), and 5% damped absolute acceleration response spectra (SAs). After classifying the training and test sets from the database, the trained DNN models were applied on the test set to evaluate the performance of the predicted results. The accuracy assessment by the residuals, R-squared ( R2), and root mean square error (RMSE) between the predicted and observed values in the test set revealed the superior performance of the proposed model compared with the traditional GMPE with proxy-based site effects such as V S30s especially in predicting both the spectral amplitude and shape of SAs.

Experimental Investigation on Axial Strength Improvement of Cold-Formed Steel Jacketed Concrete Stub Columns

This paper presents an experimental study on the behavior of low-strength concrete columns confined with cold-formed steel under axial compression. The laboratory test specimens consist of four groups of rectangular concrete stub columns in the size of 130 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 200 mm cross section and 300 mm height; the first group composes of the unconfined specimens, while the other three contain the confined specimens under 0%, 25% and 50% sustained axial loads. The jackets are made of two G450-grade channel cold-formed steel sections of 2.4 mm thickness welded together. No bonding material is used between the core concrete and the steel jacket. From the results, it is found that the cold-formed steel jacketing can increase the axial strengths of the unconfined concrete specimens by approximately 40-65%. The strength increase comes mainly from the confinement action, as only small axial deformation is detected in the jacket. Based on the given amount of prescribed preloads in this study, the presence of preload in the column does not have a significant effect on the increase in strength of the confined concrete columns. The measured strength enhancement ratio and the confinement ratio of the tested specimens are compared using five existing strength predictive equations. The performance of the unbonded cold-formed steel jacketing technique adopted in the stub columns is observed to closely conform with the predictive confinement model of the concrete-filled tubes.

Experimental investigation of slit weir discharge

ABSTRACT A rectangular slit weir is used to measure small flows, namely, those flows less than 5 L/s and contraction ratios (b/B) less than 0.25. In the present study, flow tests were conducted in a laboratory setup with various contraction ratios (b/B) under models A (b/B = 1/12), B (b/B = 1/8), C (b/B = 1/6), D (b/B = 5/24), and E (b/B = 1/4). The dimensional analysis revealed that b/B, the height of the flow on the crest of the weir to the height of the weir crest (h/P), the Reynolds number (Re), and the Weber number (We) are effective parameters for predicting the discharge coefficient of the slit weir. The experimental results indicated that the discharge coefficient is variable for different contraction ratios; the maximum average discharge coefficient was achieved at the lowest b/B (Model A), with a value of 0.72. The minimum average discharge coefficient obtained in Model B equals 0.639. In addition, former prediction equations were compared with the obtained experimental data, and the minimum, average, and maximum errors were calculated. Furthermore, a relationship was established for deriving the discharge coefficient in slit weirs with R2 = 0.9212, RMSE = 0.02244, and MAE = 0.0134 based on the obtained experimental data with four effective parameters, b/B, h/P, Re, and We.

Comparison of Amino Acid Digestibility between Commercial Crossbred Pigs and Mini-Jeju Island Native Pigs.

The objectives of this study were to determine the apparent ileal digestibility and standardized ileal digestibility (SID) of crude protein (CP) and amino acids (AA) in feed ingredients, compare the ileal digestibility of CP and AA between commercial crossbred pigs and mini-Jeju Island native pigs (JINP), and develop models for estimating SID of CP and AA for commercial pigs using mini-JINP data. The study involved five crossbred commercial pigs (31.5 ± 1.6 kg of body weight and 11 weeks of age; Landrace × Yorkshire) and five mini-JINP (31.0 ± 3.2 kg body weight and 20 weeks of age). The pigs were surgically equipped with a T-cannula at the end of ileum. Each pig breed was assigned to 5 dietary treatments in a 5 × 10 incomplete Latin square design with 10 periods. Four experimental diets were formulated to contain each of soybean meal, corn gluten feed, copra meal, and sesame expellers as the sole source of nitrogen. A nitrogen-free diet was also prepared to determine basal endogenous losses of CP and AA. No interaction between breed and feed ingredient was observed for the digestibility of CP and all indispensable AA. The SID of CP and all indispensable AA, except Arg, His, and Lys, did not differ between the two breeds of pigs. Prediction equations were developed for SID of CP and AA of commercial pigs using the SID values of mini-JINP: SID of CP (%) = (1.02 × SID of CP in mini-JINP) - 5.20 with r2 = 0.97 and p < 0.05; SID of Lys (%) = (1.12 × SID of Lys in mini-JINP) - 9.10 with r2 = 0.98 and p < 0.05; and SID of Met (%) = (1.08 × SID of Met in mini-JINP) - 4.27 with r2 = 0.96 and p < 0.05. The digestibility for most AA in feedstuffs for commercial pigs can be estimated using data from mini-JINP.

PSLBI-21 Interactive effects of dietary betaine supplementation and soybean hulls inclusion on growth performance and carcass composition of young growing pigs

Abstract Growing barrow pigs [n = 24; Large White x Landrace x Duroc; 29.51 ± 3.24 kg initial body weight (BW)] were assigned to 4 treatments in a 2 x 2 factorial design to evaluate the synergistic effects of dietary levels of betaine (BET; 0 and 0.1%) and soybean hulls (SBH; 0 and 15%) on growth performance and carcass composition. Pigs were blocked by initial BW and housed in individual pens with ad libitum access to feed and water for a 28-d feeding period. Pigs were weighed and feed intake was recorded at 14-d intervals. Real-time ultrasonic (RTU) measurements of the 10th-rib longissimus dorsi muscle area (LMA) and off-midline backfat thickness (BF) were obtained initially and on d 28 for all pigs. Prediction equations were used to estimate total fat tissue (TOFAT) and fat-free total lean (FFTOLN) from the measurements. All data were analyzed using the MIXED procedure of the SAS software. Growth performance data were analyzed as repeated measures on time and carcass data were analyzed as single measures with the respective baseline values used as covariate in the statistical model. There were no SBH x BET interactions for growth response variables. Pig growth was not affected by dietary BET supplementation. During the first 14 d of the study, pigs fed 15% SBH had decreased average daily gain (ADG; P = 0.01), average daily feed intake (ADFI; P = 0.02), and feed efficiency [gain:fee; (G:F;P = 0.04)] when compared with pigs fed diets without SBH. From d 15 to 28, dietary addition of 15% SBH reduced ADFI (P = 0.01), whereas no significant effects on the other growth performance variables were observed. Overall, when compared with pigs fed diets without SBH, pigs fed diets with 15% SBH had reduced feed intake (P = 0.01) and ADG (P = 0.04), which resulted in lighter pigs (P = 0.03) at the completion of the feeding trial. A SBH x BET interaction (P = 0.04) was observed for LMA, with decreased values (P &amp;lt; 0.05) detected for BET-supplemented pigs only when SBH was not included in the diet. No additional carcass composition traits were altered by BET supplementation. Including 15% SBH to the diet decreased BF (P = 0.05) and TOFAT (P = 0.04) in growing pigs. Neither BET supplementation nor SBH inclusion affected FFTOLN. In conclusion, adding supplemental BET to diets with SBH did not synergistically alter nutrient partitioning of young growing pigs. Although no beneficial effects of BET supplementation on growth and carcass composition traits were observed, feeding pigs diets containing 15% SBH resulted in slower growth rates with less fat deposition.

Evaporation from thin porous Coatings: Pore size effects and predictive equation for homogeneous coatings

Evaporation of small water droplets on solids is hindered because surface tension pulls the droplet into a spherical cap that has a small perimeter. Our solution is to coat a solid with a very thin, porous layer into which the droplet flows to create a large-area disk with concomitant high rate of evaporation. We investigate evaporation by varying factors that have not been previously considered: pore size and distribution, contact angle, temperature, and relative humidity (RH).A larger pore size resulted in faster evaporation, which we explain through faster transport within the coating. Even faster evaporation occurred for a bilayer structure with small particles on the air side and larger particles on the solid side. The water advancing contact angle had an insignificant effect in the range from < 10° through to 60°.Our results for different pore sizes, temperature, humidity, and contact angle all collapse onto a single curve when appropriately normalized. This validates an equation that can be used for the evaporation from a homogeneous coating that depends only one empirical factor and the droplet volume. Since the volume is often user-controlled, we envisage that this equation can be used to predict evaporation and guide design of fast-drying coatings.

PSXII-14 Fecal near infrared reflectance spectroscopy for indirect prediction of methane emissions from grazing cattle

Abstract Methane (CH4) production from enteric fermentation of ruminants accounts for over a quarter of the greenhouse gas emissions from the agriculture sector in the U.S. Accurate and precise quantification of CH4 is essential to identify strategies for mitigating CH4 emissions. Numerous techniques and methodologies exist for measuring enteric CH4 emissions from ruminants, however, many were designed for specific research purposes, potentially limiting their applicability on commercial farms. Our objective was to evaluate the application of fecal near infrared reflectance spectroscopy (FNIRS) to provide inputs for indirect CH4 prediction calibrations. The Grazingland Animal Nutrition Laboratory (GAN Lab) has produced FNIRS predictions of grazing livestock diet quality since 1995 in conjunction with animal performance (weight/body condition change) generated by the Nutritional Balance Analyzer (Nutbal) Software package. FNIRS is a non-invasive approach that provides a significant opportunity to inform models for grazing cattle CH4 emissions. From a comprehensive U.S. dataset of approximately 30,000 samples, we selected a subset representing beef cattle grazing the gulf coast region of Texas from 2016 to 2019 (n = 85). Inputs of diet crude protein (CP), diet digestible organic matter (DOM), live weight (LW), and estimated live weight change (LWC) were used to predict feed intake (FI) employing a published equation and, from outputs generated by Nutbal, i.e. FI_Eq and FI_Nut, respectively. The FI (kg DM/animal.day) generated by both methods were applied to established prediction equations to generate preliminary estimates of CH4 emissions (CH4 FI_Eq and CH4 FI_Nut, respectively). Differences among estimations were assessed through ANOVA, with significance determined at P &amp;lt; 0.05. The estimated FI was different (P &amp;lt; 0.01) for both methodologies, resulting in different (P &amp;lt; 0.01) predictions of CH4 emissions. The CH4 prediction for CH4 FI_Eq was 0.24 ± 0.03 kg/animal.day, compared to 0.38± 0.06 kg/animal.day for CH4_Nut. A difference was observed between years for the CH4 predictions estimated with CH4 FI_Eq (P &amp;lt; 0.01). The FNIRS/Nutbal technique provided useful inputs to the established CH4 calibrations but will need to be validated against CH4 reference methods. The Gan Lab FNIRS/Nutbal dataset offers an opportunity to not only estimate U.S. grazing ruminant CH4 emissions over the past 30 years, but also predictions of current and future emissions at individual operation to national scales. This technique will enable producers with the knowledge to optimize forage resources management, mitigate CH4 emissions, and enhance the sustainability of grazing animal agriculture.

A machine learning-driven modeling and optimization approach for enhancing cassava mash production quality in cassava graters

Machine performance modeling and optimization have emerged as crucial steps for process enhancement and efficiency. This study explored machine learning to model and optimize the cassava grating chamber of cassava grater for the quality production of gari. This domain remains unexplored thus far. A total of 196 graters were studied. Key variables studied included tooth diameter (TD), tooth height (TH), inter-tooth spacing (ITS), drum speed (DS), clearance (C), and moisture content of cassava (MC). Geometric mean diameter (GMD) represented mash quality. Feature importance rankings emphasized TH (0.488784), C (0.243284), TD (0.112682), ITS (0.103547), DS (0.036261), and MC (0.015442) in determining particle size (GMD) of grated mash. Machine learning models efficiently interpreted these attributes, including gradient boost regressor, linear regression, neural network, and random forest. The Gradient boost regressor was the best predictive model achieving 95.34 % accuracy, RMSE (0.3291), and MAE (0.2303). The study provides a GMD predictive equation and optimized parameters for specific gari sizes production, offering valuable insights for tailored machinery in the cassava grating activity.

Maximum temperature of steel bridges due to multiple under-bridge fire sources

In this study, an analytical model was developed and predictive equations were proposed to estimate the maximum temperature of steel bridges in the event of a fire originating from complex facilities beneath the bridge. Based on statistical data, a representative shape of steel bridges in South Korea was selected. A representative Heat Release Rate (HRR) curve for the complex facility was selected, and a parametric study was conducted, including variables such as Heat Release Rate Per Unit Area (HRRPUA), fully developed time, vertical clearance, and the area of the complex facility. Through correlation analysis, key parameters influencing the maximum temperature in steel bridges were examined. Based on the results, both linear and non-linear temperature prediction equations were suggested. The non-linear equation demonstrated higher predictive performance than the linear equation. This study provides foundational data for enhancing the accuracy of fire analysis methods and temperature prediction models for steel bridges.

96 Using greenhouse gas flux to estimate forage dry matter intake in ad libitum fed replacement heifers

Abstract An accurate estimate of feed intake is a critical component in determining individual animal feed efficiency. However, existing feed intake measurement systems are not designed for unprocessed, dry forage diets. Open-circuit gas flux technology measures enteric gas emissions for cattle in a pasture-based setting. If a significant relationship exists between feed intake and gas emissions, this system could be used to rank grazing cattle for forage intake and forage utilization efficiency. Our objective was to develop prediction equations to estimate forage dry matter intake using gas flux and performance data. Angus and Angus x Hereford heifers [n = 71; initial body weight (BW) 329.5 ± 31.1 kg] were used over 3 yr to measure intake, gain, and gas flux. All heifers were subject to a 21-d adaptation and 71-d intake test using individual intake technology (SmartFeed; C-Lock Rapid City, SD) with a stocking rate of 3.3 heifers per feeder. Throughout the study, heifers were fed a long-stem, unprocessed, grass hay diet (≥ 10.8% CP, 2.0 Mcal ME, and 56% TDN) with ad libitum access to a salt/mineral supplement. The GreenFeed system (C-Lock Rapid City, SD) was used to monitor oxygen (O2) consumption (means ± SD; 3364.4 ± 374.8 g/d), methane (CH4; 181.4 ± 24.3 g/d) and carbon dioxide (CO2) production (4,965.3 ± 494.5 g/d). Body weights were collected every 14 d, and average daily gain (ADG) was calculated by regressing BW on day of study. The phenotypic correlations of forage DMI with CO2, CH4, O2, and heat production (HP) were 0.72, 0.66, 0.74, and 0.74, respectively. Average daily gain and midpoint metabolic BW combined with the estimate of daily HP, O2, or CO2 accounted for 60% of the variation in forage DMI. These results suggest that forage intake can be predicted using performance data in conjunction with the GreenFeed gas quantification system.