Prediction Equations Research Articles

A new experimental setup for the determination of drying soil–water characteristic curve and coefficient of permeability using the continuous evaporation method and osmotic tensiometers

Soil-water characteristic curve (SWCC) and coefficient of permeability (k) are amongst the most crucial soil properties in unsaturated soil mechanics for soil moisture conservation. The direct measurement and prediction of such properties are important to engineering applications, but they are complicated. This study developed a new experimental setup (the OT permeameter) using osmotic tensiometers (OTs) and the continuous evaporation method. The proposed OT permeameter could directly measure SWCC and k up to 1000 kPa suction within one to two weeks. Moreover, the proposed setup is fully automated and data processing was minimal. In addition, a new general equation has been developed to predict and best fit the unsaturated k measurement. The proposed equation uses a minimal number of parameters that carry physical meaning and provides an intuitive and easy way to predict and best-fit k measurement. The accuracy and robustness of both the proposed OT permeameter and prediction equation were evaluated using published data and experimental data from this study.

Bond behaviour of staggered/non-staggered lap-spliced GFRP bars in concrete

This paper reports on the results of an experimental and analytical investigation into the bond behaviour of glass fibre-reinforced polymer (GFRP) splices in concrete. Eleven full-scale beam specimens in which GFRP bars were spliced within the constant-moment zone are examined. The study incorporates several key test variables, encompassing the ratio of bars lapped (33%, 50%, and 100%), surface characteristics (including sand-coated, ribbed, and wrapped bars), bar diameter (12 mm and 16 mm), and splice length (ranging from 30 to 40 times the bar diameter). The findings reveal a reduction in bond strength ranging from 12 to 24% for staggered bars when compared to their non-staggered counterparts. Furthermore, an increase in bar diameter is shown to decrease bond strength by 16%, whilst surface treatment also influences both bond strength and the pattern of cracking in the splice region. Increasing the splice length from 30 to 40 times the bar diameter enhances bar failure stress by 26%, but it also reduces the average bond strength by 9%. A new predictive equation for the design bond strength of fibre-reinforced polymer (FRP) bars is developed by utilizing a data base of 151 test results. The predicted bond strengths demonstrate greater consistency with experimental outcomes in comparison to previous models.

Residual mechanical properties of butt-welded ultra-high-strength steel(Q890 and Q960)after fire exposure

At present few studies have considered the mechanical properties of welded ultra-high strength (UHSS) steel after fire. In this paper, two kinds of ultra-high-strength steels Q890 and Q960 are selected as research object, and the GMAW welding method is used to connect them. An experimental study is conducted to reveal the mechanical properties of welded UHSS specimens after exposure to different high temperatures. The butt-welded UHSS and the corresponding base metal specimens are firstly heated from 200 °C to 1000 °C. After natural cooling to room temperature, the specimens are then subjected to a tensile test, and the stress-strain curves and different fracture modes are obtained. The results shown that temperature, HAZ and softening effect have an impact on the fracture mode and mechanical properties of butt-welded UHSS. Secondly, the variation law of elastic modulus of butt-welded UHSS with increasing temperature is similar to that of UHSS base metal. However, as the temperature increases, the corresponding weakening rates of yield strength and ultimate strength are significantly lower than those of the base material. Finally, based on the experimental results, prediction equations for the reduction factors of mechanical properties indicators of UHSS (base metal and welded) and HSS after fire exposure are proposed, and a comparative analysis is conducted with the existing methods. The results shown that the predictive equations proposed in this paper can more accurately calculate the mechanical properties reduction factors of UHSS (base metal and welded) and HSS after fire.

Development of web-based software for environmental impact assessment focusing on road traffic noise

In the Republic of Korea, Environmental Impact Assessment (EIAs) precedes development projects to predict and analyze potential environmental effects. Generally, EIA noise evaluations utilize 2D noise prediction equations and correction coefficients. This method, however, offers only a sectional noise evaluation and has limitations in complex environments with diverse noise sources. Moreover, the determination of various variables during the EIA process based on subjective human judgment raises concerns about the reliability of the results. Thus, this study aims to develop software accessible via a web environment for user-friendly EIA noise evaluations. This software supports integrated data management and generates a 3D noise prediction model for more precise and realistic analysis of noise impacts, specifically focusing on road-traffic noise at this stage of development. The 3D noise prediction model and noise map generated by the developed software have been validated against through comparison with the results of onsite noise measurements and commercial EIA software, SoundPLAN. This validation aimed to assess the practical utility of the application.

Measured and predicted resting metabolic rate of Dutch and Norwegian Paralympic athletes.

While the resting metabolic rate (RMR) is crucial for understanding athletes' energy requirements, limited information is available on the RMR of Paralympic athletes. To determine RMR and its predictors in a diverse cohort of Paralympic athletes and evaluate the agreement between measured and predicted RMR from both newly developed and preexisting equations. This cross-sectional study, conducted between September 2020 and September 2022 in the Netherlands and Norway, assessed RMR in Paralympic athletes by ventilated hood indirect calorimetry and body composition by dual energy X-ray absorptiometry. Sixty-seven Paralympic athletes (male: n=37; female: n=30) competing in various sports, with a spinal cord disorder (SCD; n=22), neurological condition (n=8), limb deficiency (n=18), visual or hearing impairment (n=7) or other disability (n=12) participated. RMR, fat-free mass (FFM), body mass, and triiodothyronine (T3) concentrations were assessed. Multiple regression analyses were conducted with height, FFM, body mass, sex, T3 concentration, and disabilities as potential predictors of RMR. Differences between measured and predicted RMRs were analyzed for individual accuracy, root mean square error (RMSE), and intraclass correlation (ICC). Mean RMR was 1386±258 kcal/day for females and 1686±302 kcal/day for males. Regression analysis identified FFM, T3 concentrations and the presence of a spinal cord disorder (SCD), as the main predictors of RMR (adjusted R2=0.71; F=50.3; P<0.001). The novel prediction equations based on these data, as well as pre-existing equations of Chun et al. and Nightingale and Gorgey performed well on accuracy (>60% of participants within 10% of measured RMR), had a good reliability (ICC >0.78), and low RMSE (≤141 kcal). FFM, total T3 concentrations, and the presence of SCD are the main predictors of RMR in Paralympic athletes. Both the current study's prediction equations and those by Chun et al. and Nightingale and Gorgey align well with measured RMR, offering accurate prediction equations for the RMR of Paralympic athletes.

Adsorption simulation of 2,4-D pesticide on novel zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complexes using machine learning approach.

The capacity of zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complex (Zn(NH2-TBA)2) and modified Zn(NH-TBA)2COMe complex for removal of 2,4-dichlorophenoxyacetic acid (2,4-D) from aqueous solutions was investigated through adsorption modeling and artificial intelligence tools. Analyzing the adsorption characteristics of pesticides helps in studying the groundwater pollution by pesticides in agriculture area.In this study, Zn(NH2-TBA)2 was synthesized using Schiff base and its surface was modified using acetic anhydride group and their physical characteristics were identified using proton NMR, FTIR, and XRD. NMR results showed maximum modification yield obtained was 65% after 5days. The porous structure and surface area monitored using nitrogen isotherm and BET surface area analysis presented relatively less surface area and porosity after modification. Adsorption modelling indicated that Toth model with a maximum adsorption capacity of 150.8mg/g and 100.7mg/g represents the homogenous adsorption systems which satisfy both low- and high-end boundary of adsorbate concentration in all settings according to the optimum point, while the kinetics and rate of 2,4-D adsorption follow the pseudo-first-order kinetic model in all situations. Artificial neural network (ANN), support vector regression, and particle swarm optimized least squares-support vector regression (PSO-LSSVR) were used for the optimization and modelling of adsorbent mass, adsorbate concentration, contact time, and temperature to develop predictive equations for the simulation of the adsorption efficiency of 2,4-D pesticide. The obtained results exhibited the better performance of ANN and PSO-LSSVR for prediction of adsorption results. The mean square error values of ANN (0.001, 0.012) and PSO-LSSVR (0.121, 0.105) were obtained for Zn(NH2-TBA)2 and Zn(NH-TBA)2COMe, respectively, while their respective coefficient of determination (R2) obtained were 0.999 and 0.988 for ANN and 0.980 and 0.825 for PSO-LSSVR. The study specified that machine learning predictive behavior performed better for Zn(NH2-TBA)2 compared to Zn(NH-TBA)2COMe that is also supported by theoretical kinetics and isotherm models. The research concludes that artificial intelligence models are the most efficient tools for studying the predictive behavior of adsorption data.

Comparison of Cardiorespiratory Fitness Prediction Equations and Generation of New Predictive Model for Patients with Obesity.

Cardiorespiratory fitness (CRF) is a critical marker of overall health and a key predictor of morbidity and mortality, but the existing prediction equations for CRF are primarily derived from general populations and may not be suitable for patients with obesity. Predicted CRF from different non-exercise prediction equations was compared with measured CRF of patients with obesity who underwent maximal cardiopulmonary exercise testing (CPET). Multiple linear regression was used to develop a population-specific non-exercise CRF prediction model for treadmill exercise including age, sex, weight, height and physical activity level as determinants. 660 patients underwent CPET during the study period. Within the entire cohort, R2 values had a range of 0.24-0.46. Predicted CRF was statistically different from measured CRF for 19 included equations. Only 50% of patients were correctly classified into the measured CRF categories according to predicted CRF. A multiple model for CRF prediction (ml/min) was generated (R2 = 0.78) and validated using two cross-validation methods. Most used equations provide inaccurate estimates of CRF in patients with obesity, particularly in cases of severe obesity and low CRF. Therefore, a new prediction equation was developed and validated specifically for patients with obesity, offering a more precise tool for clinical CPET interpretation and risk stratification in this population.

Post-fire performance of iron-based shape memory alloy: Experimental study, prediction equations and constitutive models

To expand the application of iron-based shape memory alloy (Fe-SMA) for fire resilience improvement, this paper presents a systematic investigation on the mechanical properties of post-fire Fe-SMA. In tests, the specimens of China-made Fe-SMA were heated to target temperatures up to 1000 ℃, then cooled to room temperature in air or water, and finally subjected to tensile tests. The variation trends of mechanical properties with different cooling methods were analyzed, accompanied by the residual factor comparison with high strength steels Q460, S460, A36, A572 and A992, as well as with austenite stainless steel S30408. Finally, the prediction equations of residual factors and the post-fire constitutive model of Fe-SMA were proposed. Experimental results indicate that the exposed elevated temperature and cooling method significantly influence the post-fire performance of Fe-SMA. Compared with commonly used high strength steel and austenite stainless steel in civil engineering, Fe-SMA has unique post-fire performance, with a noticeable enhancement after exposure to elevated temperatures below 800 ℃. Therefore, the targeted theoretical prediction of post-fire Fe-SMA performance is significant and can be used as the investigation foundation for the fire resilience improvement application of Fe-SMA in civil engineering.

Bayesian maximum entropy interpolation analysis for rapid assessment of seismic intensity using station and ground motion prediction equations

In this paper, we explored the combination of seismic station data and ground motion prediction equations (GMPE) to predict seismic intensity results by using Bayesian Maximum Entropy (BME) method. The results indicate that: 1) In earthquake analysis in Japan, soft data has predicted higher values of intensity in disaster areas. BME corrected this phenomenon, especially near the epicenter. Meanwhile, for earthquakes in the United States, BME corrected the erroneous prediction of rupture direction using soft data. 2) Compared with other spatial interpolation methods, the profile results of Japan earthquake and Turkey earthquake show that BME is more consistent with ShakeMap results than IDW and Kriging. Moreover, IDW has a low intensity anomaly zone. 3) The BME method overcomes the phenomenon that the strength evaluation results do not match the actual failure situation when the moment magnitude is small. It more accurately delineates the scope of the disaster area and enriches the post-earthquake processing of disaster area information and data. BME has a wide range of applicability, and it can still be effectively used for interpolation analysis when there is only soft data or few sites with data available.

Crop Phenology Estimation in Rice Fields Using Sentinel-1 GRD SAR Data and Machine Learning-Aided Particle Filtering Approach

Abstract. Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data.

Evaluation of Rotation Capacity and Bauschinger Effect Coefficient of I-Shaped Beams Considering Loading Protocol Influences

Recent catastrophic earthquake events have reinforced the necessity of evaluating the seismic performance of buildings. Notably, the buildings can go into the plastic phase during a striking earthquake disaster. Under this condition, the current design codes assume seismic response reduction by virtue of the energy dissipation capacity of the structural members. In the strong-column–weak-beam design, which involves I-shaped beams and boxed columns, the mechanism is defined as a standard design scheme to prevent the building from collapsing. Therefore, energy dissipation relies highly on the I-shaped beam performance. However, the I-shaped beam performance can differ depending on the loading history experienced, whereas this effect is untouched in the prevailing evaluation equation. Hence, this study first performs cyclic loading tests of 11 specimens using different loading protocols. The experimental results clarify the fluctuation in the structural performance of I-shaped beams depending on the applied loading hysteresis, proving the necessity of considering the stress history for proper assessment. Furthermore, the database of experimental results is constructed based on the previous experimental studies. Ultimately, the novel evaluation equation is proposed to reflect the influences of the loading protocol. This equation is demonstrated to effectively assess the member performance retrieved from the experiment of 65 specimens, comprising 11 specimens from this investigation and 54 specimens from the database. The width–thickness ratio, shear span-to-depth ratio, and loading protocols are utilized as the evaluation parameters. Moreover, the prediction equation of the Bauschinger effect coefficient is newly established to convert the energy dissipation capacity under monotonically applied force into hysteretic energy dissipation under the cyclic forces.

Curing parameters development and long-term performance of ohmic heating cured conductive cementitious composites: A comparative study on fiber fraction

Ohmic heating (OH) curing is a new method for concrete production in cold areas, while the changing regularity for the curing parameters including electric voltage, current, resistivity and curing temperature during the curing process, and the long-term performance evolution law are still unrevealed. In this research, this method was used to fabricate carbon fibers (CFs) reinforced conductive cementitious composites under −20 °C, the early-age curing parameter and the long-term performance evolution law of the specimen were clarified. Higher CFs dosage showed benefits on the electrical conductivity and heating performance of OH pre-cured specimens. To be specific, simulation and experimental results evidenced that the electrical conductivity of fresh mixture was much improved with 0.6 vol% CFs with the electrical resistivity decremental ratio of 74.82 %. Also, the curing temperature was enlarged in the presence of 0.75 vol% CFs with the incremental ratio around 50 %, indicating the much-improved heating efficiency. Strength test results showed that 1-day OH pre-cured specimens possessed the comparable 150-day strength with RT prepared specimens. Moreover, the hydration products and pore structure test results further verified the feasibility of OH curing with high hydration forming ability and denser microstructure at longer duration. In addition, the “RER” was introduced to evaluate the variation law of electrical resistivity in further curing process, and the corresponding prediction equation was derived.

UHPC modulus of elasticity: Assessment and new developments using companion materials and structural data

The determination of the concrete modulus of elasticity (MOE) plays a crucial role in the analysis and design of structural reinforced and prestressed concrete elements. Various empirical MOE prediction equations have been developed for UHPC. However, most of the existing models have been typically calibrated or developed based on data from one type of UHPC and material tests only. As such, no previous work looked at the MOE prediction equations validity at a large-scale structural level and for the different range of UHPC mixtures that span from economic or non-proprietary lower-end mixtures to robust carbon nanofiber (CNF)-enhanced and high-end mixtures. This research fills this knowledge gap by revisiting the topic of UHPC MOE prediction equations using exclusive companion datasets of materials and full-scale structural tests that also incorporate, for the first time, four different types of UHPC mixtures. In this study, MOE values are estimated from experimentally determined axial stiffness of 22 full-scale UHPC columns with varying cross-sections in conjunction with hundreds of cylinders. The study first compared cylinders and columns-based MOE values, then used the extensive experimental datasets to assess recent and relevant UHPC MOE prediction equations, including what has been proposed for the AASHTO UHPC Structural Design Guide. The results show that while some of the existing prediction models are accurate for traditional UHPC mixtures, all models consistently underestimate MOE for CNF-enhanced UHPC and overestimate it for economic UHPC mixtures with local sand and cement. The paper concludes with a recommendation to account for UHPC mixture type and proposes new modifications for adopting MOE prediction equations for all types of UHPC.

Personalized lung care: Bronchopulmonary dysplasia risk prediction tool tailored for neonates born in resource-limited settings.

Predicting bronchopulmonary dysplasia (BPD) to assess the risk-benefit of therapy is necessary considering the side effects of medications. We developed and validated an instrument for predicting BPD and compared it with an instrument currently used for neonates born in a Brazilian hospital. This was a retrospective cohort study of patients born between 2016 and 2020 with a gestational age (GA) between 23 and 30 weeks. Predictive equations were elaborated using methods of component variable selection collected on the 14th day of life; 70% of the sample was randomly selected for the construction of risk prediction equations and the remaining 30% for their validation, application, and comparison with the National Institute of Child Health and Human Development (NICHD) instrument. The sensitivity, specificity, and predictive values of the equations were calculated. The equation that used variables with p < 5% in Fisher's exact test presented the best results: specificity of 98% and positive predictive value of 93% and could be used for BPD prediction of all small-for-gestational-age (SGA) infants. The NICHD calculator applied to our population had a specificity of 93% and a positive predictive value of 75% and could not be applied to extremely SGA infants. Our tool can predict the risk of BPD on the 14th day of life, has higher specificity and positive predictive value to our population than the NICHD instrument, and can be suitable for SGA infants. The results must be confirmed by applying it to other populations to validate our tool.

Modelling soil-water retention curves subject to multiple wetting-drying cycles: An approach for expansive soils

This study proposes an innovative and simple method to quantify the effect of wetting–drying history on soil–water retention curve (SWRC) of expansive soils. This method is based on the increment relationship between degree of saturation and initial void ratio corresponding to irreversible swelling or shrinkage after each wetting–drying cycle, following the double-structure scheme for three-phase reactive porous media. The approach satisfies the intrinsic constraints for partially saturated porous media, and the incremental relationship can be applied in any existing SWRC equations for future water retention capacity prediction. In this respect, only one new rate function is proposed, which could be easily calibrated by relevant experimental data. The prediction of the model is verified through the comparison with relevant experimental data of two expansive clayey soils. To evaluate the general applicability of the proposed method, two typical SWRC equations proposed in literature were used. The results showed a very good agreement with experimental data subject to multiple wetting–drying cycles, indicating its potential as an effective tool for estimating preliminary SWRCs of expansive soils.

On-Site Earthquake Early Warning Model for Selected Records in the NGA-West2 Dataset Using S- and P-Wave Spectral Ratios

ABSTRACT On-site earthquake early warning (EEW) requires the best estimates of earthquake magnitude and distance parameters within a few seconds after the P-wave arrival for estimating the subsequent S-wave parameters. The errors in the estimates of the earthquake P-wave parameters will propagate into the estimates of the S-wave parameters. To solve this problem, we used the methodology by Zhao and Zhao (2019), which uses the spectral ratio R3P between the response spectral values of the first 3 s of the S wave and that of the first 3 s of the P wave, referred to as the R3P model. The modeling presented here was based on strong-motion records from the Next Generation Attenuation (NGA)-West2 dataset. We also used the spectral ratio RFP between the response spectral values from the full records from the S-wave arrival time to the end of the record and that of the first 3 s P wave to develop a second EEW model (the RFP model). An advantage of these two models is that the magnitude and hypocentral distance are not required in considerable magnitude and distance ranges. This means that the errors in the estimated source and path parameters from the first 3 s of the P wave will not affect the model predictions. A theoretical justification for these results is that the magnitude and the distance scaling rates for the first 3 s of the P wave are similar to those of the first 3 s of the S wave. This may also apply to the full S-wave window within useful EEW magnitude and distance ranges. We also found that when the estimated magnitude and distance for a record are necessary, the effect of the corresponding errors would be smaller than using a ground-motion prediction equation (GMPE), because the magnitude and distance scaling rates from this study are smaller than those of many GMPEs.

Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach

Since the cooling systems used in buildings in hot climates account for a significant portion of the energy consumption, it is very important for both economy and environment to accurately predict the cooling load and consider it in building designs. This study aimed to maximize energy efficiency by appropriately selecting the features of a building that affect its cooling load. To this end, data-driven, accurate, and accessible tools were developed that enable the prediction of the cooling load of a building by practitioners. The study involves simulating the energy consumption of a mid-rise, double-story terrace house in Malaysia using building information modeling (BIM) and estimating the cooling load using ensemble machine learning models and genetic programming. Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) models have been developed and made available as an online interactive graphical user interface on the Streamlit platform. Furthermore, the symbolic regression technique has been utilized to obtain a closed-form equation that predicts the cooling load. The dataset used for training the predictive models comprised 94,310 data points with 10 input variables and the cooling load as the output variable. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used to measure the predictive model performances. The results of the machine learning models indicated successful prediction, with the CatBoost model achieving the highest score (R2 = 0.9990) among the four ensemble models and the predictive equation. The SHAP analysis determined the aspect ratio of the building as the most impactful feature of the building.

PSI-21 Optimizing sample size for accurate measurements and prediction equations for intestinal morphology in broiler chickens

Abstract Histological measurements of intestinal morphology, including villi height (VH) and crypt depth (CD), are routinely used to evaluate intestinal health and the nutrient absorptive capacity in monogastric animals. These metrics suffer from significant natural variation, and there is no established standard for the number of unique measurements required to robustly estimate these variables for a given animal. Furthermore, the potential utility of additional variables, including villus width (VW), perimeter (VP), or villus area (VA), has not been explored. Therefore, the objective of the current experiment was to estimate the minimum number of measurements required to control the intrasample variance in each variable and determine if more complex or time-consuming metrics, such as VP and/or VA, can be accurately estimated from linear measurements. A set of 1283 intestinal morphology data, including VH, CD, VW, VP, and VA, measured from 68 intestinal tissues (46 ileum and 22 jejunum) of 4 experiments were used in the current study (Figure 1). Retrospective calculation of the CV for all combinations of N measurements per sample was used to determine the impact of measurement number on the quality of data. Data for the jejunum and ileum were pooled together, and the NLIN procedure of SAS was used to estimate the optimal measurement for intestinal morphology measurements using a quadratic broken line. The VA was estimated using geometric calculation with the assumption that VA equals 0.67 × base width × VH. A paired t-test was conducted to compare measured and calculated VA. The optimal number of measurements to minimize within-sample CV for VH, CD, VW, VP, and VA ranged from 10.6 to 10.8. The VH was highly correlated (P &amp;lt; 0.05) with VP (r = 0.95 and 0.87) and VA (r = 0.77 and 0.63) in the jejunum and ileum, respectively (Table 1 and 2). Additionally, there was a significant difference (P &amp;lt; 0.05) between measured (0.110 µm2) and calculated VA (0.086 µm2). In conclusion, this study shows that measuring 11 villi per sample is required for minimizing variations within the jejunal or ileal tissue in broiler chickens. Also, estimating VA based on idealized geometry was found to be inaccurate, necessitating either direct measurement or more complex prediction equations.

111 Current and future usage of soybean meal in swine diets in China

Abstract The objective of this presentation was to introduce the utilization tendency and research progress of soybean meal (SBM) in swine diets in China. As for the utilization tendency of SBM in China, it not only brings great pressure to the food security of China, but also directly pushes the high production cost of animal products. In 2022, 117 million tons of protein feed was consumed by the animal husbandry in China, accounting for nearly 1/3 of the total protein feed consumption in the world. Thus, the application of low-protein diet has been promoted with the strong support of the Ministry of Agriculture and Rural Affairs in China since 2018. With the related techniques, including popularizing low protein diet, developing new protein feed resources, promoting precise nutrition, and feeding technology, even though the compound feed output has increased by more than 84 million tons in 2022 in China, the SBM consumption in feed has reduced 6%. Recently, the geopolitical challenges, regional unrest and climate change tendency have increased the great uncertainty of protein feed supply, which requires us to deeply understand the significance of SBM reduction and substitution in animal feed. As for the research progress, the available energy contents of SBM were determined on growing pigs. Barrows [n = 24; initial body weight (BW) = 36 ± 1.4 kg] were randomly allotted to 4 diets containing SBM with either corn basal or corn-soybean meal basal, and total feces and urine collection method and indirect calorimetry method were used to determine the available energy of SBM. The results showed that the average digestible energy (DE), metabolizable energy (ME), net energy (NE), and retained energy (RE) values in SBM [17.36, 16.52, 10.62, and 5.06 MJ/kg dry matter (DM), respectively] determined using the corn-SBM-basal diet were not different from the those determined using the corn-basal diet. Moreover, the best fitted prediction equations developed based on 22 SBM samples produced from soybeans from China (n = 6), USA (n = 6), Brazil (n = 7) or Argentina (n = 3) were: DE = 38.44 - 0.43 CF - 0.98 GE + 0.11 ADF (R2 = 0.67, P &amp;lt; 0.01) and ME = 2.74 + 0.97 DE - 0.06 CP (R2 = 0.79, P &amp;lt; 0.01). The in vitro digestion kinetics of SBM from different sources showed that the release of amino acids in the 0-10 min time interval was significantly negatively correlated with the NE of SBM (R2 = -0.34).

106 Net energy of soybean meal

Abstract Our current net energy (NE) systems were developed based on work conducted around 40 yr ago. At that time, it was normal that pigs deposited no more than 40% of the protein they were fed. However, modern genetic lines are much leaner than older genetics and they deposit more protein than earlier. It is, therefore, not uncommon that protein deposition in modern genotypes exceeds 60% of dietary protein intake. This means that more of the amino acids in the protein-rich ingredients are used for protein deposition and less are being deaminated with a subsequent reduced energetic cost of deamination associated with feeding protein-rich ingredients. As a consequence, the theoretical energy value of protein rich ingredients such as soybean meal (SBM) is greater when fed to modern pigs than to older genotypes that were used when our current NE systems were developed. Indeed, in several recent experiments it was demonstrated that the ME and NE of SBM is greater than indicated in current feed ingredient tables. A greater NE than predicted has also been demonstrated for field peas, whereas corn does not seem to have increased energy value compared with previous estimates, which is consistent with the hypothesis that it is the increased protein efficiency of modern pigs that is the reason for the greater NE value in SBM. As a consequence of the greater NE value of SBM, less added fat need to be used to reach a certain NE level in the diet, which will reduce overall diet costs. However, because of the greater genetic capacity for protein deposition in modern pigs, there is a need to upgrade the NE system and to develop new equations to predict NE in diets and ingredients based on the protein deposition capacity of modern pigs. It can also be argued that because commercially fed pigs always are group housed and allowed ad libitum intake of feed, pigs fed to generate prediction equations for NE also need to be group housed and allowed ad libitum intake of feed because both animal behavior and level of feed intake may affect the NE value of diets. In conclusion, because of the increased ability of modern genotypes of pigs to retain dietary protein in the body, there is reduced deamination of amino acids and therefore reduced energy loss in modern pigs than in older genotypes, which results in greater NE of protein rich ingredients including SBM. There is, therefore, a need for developing a new NE system that preferably uses group-housed pigs that are allowed ad libitum access to feed.