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Objective A study was conducted to quantify the performance differences of the near-infrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations. Methods A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters. Results The performance of the developed NIRS calibration models was evaluated based on R2, standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R2 (coefficient of determination), standard error, and RPD. Although the R2 of calibration models was mainly greater than 0.90 across the feed value indexes, the R2 of cross-validations was much lower. The R2 of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling. Conclusion Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.

Background Methods for evaluating the strength of the knee extensor muscles play a vital role in determining the functionality of the lower limbs and monitoring any alterations that occur over time in older individuals. This study assessed the validity of the Modified Squat Test (MST) in predicting knee extensor muscle strength in older adults. Methods This study included a total of 110 older adults. We collected demographic information such as sex, age, body weight, height, and thigh circumference. Muscle strength was assessed by measuring the maximum voluntary isometric contraction of the knee extensors, and by performing the MST (5 and 10 repetitions) and single-leg standing balance test. Stepwise multiple linear regression analysis was used to investigate multiple factors impacting the prediction of knee extensor strength. Results Factors such as age, sex, thigh circumference, performance on the single-leg standing eye-open (SSEO) task, and the time required to complete the 10 MST repetitions together explained 77.8% of the variation in knee extensor muscle strength among older adults. We further developed a predictive equation to calculate strength as follows: strength = 36.78 − 0.24 (age) + 6.16 (sex) + 0.19 (thigh circumference) + 0.05 (SSEO) − 0.54 (time required to complete 10 MST repetitions) ± 5.51 kg. Conclusion The 10-repetition MST is an invaluable instrument for establishing an equation to accurately predict lower limb muscle strength.

Background. Muscle hypertrophy is one of the main goals of resistance training and is frequently associated with an increase in muscle cross-sectional area (CSA). Imaging techniques are valid and reliable techniques for measuring CSA but are inaccessible to most professionals. Anthropometric-based multiple regression equations (i.e. using circumference and skinfolds outcomes) have been proposed to estimate muscle CSA describing a limited number of muscles. Purpose. The aim of this study was to develop anthropometric-based multiple regression equations using ultrasound images to estimate CSA of pectoralis major, biceps brachii, triceps brachii, rectus abdominis, rectus femoris, vastus lateralis, biceps femoris, gastrocnemius lateralis and medialis muscles at two distinct regions for each muscle assessed. Methods. Thirty trained women and men (mean ± SD; body mass = 69.6 ± 11.1 kg; stature = 168.5 ± 8.9 cm; and age = 25.7 ± 4. years) participated in the study. Circumference and skinfold measurements were taken at the same points where ultrasound images were acquired to evaluate the CSA of muscles analyzed. Muscle CSA area was assessed using panoramic-view ultrasound. Results. Seventeen multiple regression equations were developed using measures of circumference, skinfold, body weight, height and muscle CSA. Conclusions. Ten equations developed presented the relative errors between 7.2 and 19.9%. These values are comparable or less than those found in equations commonly used by sport and exercise professionals. Thus, these equations are recommended for single-time point estimation of muscle CSA. However, their usefulness for monitoring the CSA changes promoted by resistance training requires further research. [ABSTRACT FROM AUTHOR]

Prader-Willi syndrome (PWS) is a rare disorder characterised by varying nutritional phases that occur throughout the lifespan, ranging from failure to thrive to hyperphagia. If uncontrolled, the imbalance between energy intake and expenditure results in obesity development and increased morbidity and mortality risk. Although measures of energy requirements for accurate nutrition assessment are vital, the evidence appears sparse and heterogeneous; hence, the aim of this review was to examine the available literature on energy expenditure predicted or measured using various methods in individuals with PWS. Studies were sought that presented methods and results on resting energy expenditure or basal metabolic rate. A narrative synthesis was completed to present the study characteristics and results. Methods of determining energy requirements included predictive equations and indirect calorimetry. Differences amongst ages, growth hormone therapy, fasting status, and measures in which results were presented were limitations to appropriately summarising and identifying trends in energy expenditure. Indirect calorimetry was identified as the most accurate method; however, it is not widely available in all settings. Further research is encouraged to support the development of valid and reliable predictive equations that will better inform and improve the efficiency of clinical practice in supporting people with PWS. [ABSTRACT FROM AUTHOR]

Prader-Willi syndrome (PWS) is a rare disorder characterised by varying nutritional phases that occur throughout the lifespan, ranging from failure to thrive to hyperphagia. If uncontrolled, the imbalance between energy intake and expenditure results in obesity development and increased morbidity and mortality risk. Although measures of energy requirements for accurate nutrition assessment are vital, the evidence appears sparse and heterogeneous; hence, the aim of this review was to examine the available literature on energy expenditure predicted or measured using various methods in individuals with PWS. Studies were sought that presented methods and results on resting energy expenditure or basal metabolic rate. A narrative synthesis was completed to present the study characteristics and results. Methods of determining energy requirements included predictive equations and indirect calorimetry. Differences amongst ages, growth hormone therapy, fasting status, and measures in which results were presented were limitations to appropriately summarising and identifying trends in energy expenditure. Indirect calorimetry was identified as the most accurate method; however, it is not widely available in all settings. Further research is encouraged to support the development of valid and reliable predictive equations that will better inform and improve the efficiency of clinical practice in supporting people with PWS. [ABSTRACT FROM AUTHOR]

Explicitly modelling tailings consolidation behaviour contributes to improve integrated management approaches and accurately estimate the storage capacity of tailings storage facilities (TSFs) to better predict their static and dynamic stability. However, slurry tailings demonstrate a highly non-linear evolution of stiffness and hydraulic conductivity during consolidation, thus significantly complexifying the determination of their hydromechanical properties. In this study, an approach to update Mohr Coulomb parameters and simulate the continuous evolution of hydraulic conductivity and stiffness of tailings materials with the reduction of the void ratio was proposed and embedded in a finite difference code to more realistically simulate the evolution of material properties during sequential loadings. The model was validated using laboratory column tests and various predictive functions were tested to estimate hydraulic conductivity for field applications. Finally, the developed approach was applied to a simplified model of tailings impoundment to illustrate practical applications. Results from this study indicated that the approach developed was able to capture the non-linearity properties of tailings during consolidation, and that using continuously updated stiffness and hydraulic conductivity could induce significantly different magnitude and rate of consolidation than models with constant properties. Predictive models such as Kozeny–Carman and Kozeny–Carman Modified models also gave a satisfactory estimation of tailings behaviour, at least for preliminary studies. The simple modifications to the numerical codes proposed in this paper could therefore significantly improve the numerical simulation of tailings behaviour in the short term and contribute to a better planning of deposition plans. [ABSTRACT FROM AUTHOR]

This study has developed Intensity Prediction Equations (IPEs) for the Himalayas and its sub-regions (divided into North-West Himalaya, Central Himalaya, and North-East Himalaya). For this purpose, intensity data reported in previous studies using traditional methods (like field surveys, media reports, and newspapers) and internet-based questionnaires (such as USGS's Did You Feel It? or DYFI) were used to catalogue two separate intensity datasets. Intensities of traditional datasets were also reassessed for some earthquake events by different studies in the different scales of assignment, which was homogenized for the same intensity scale. IPEs are derived for both datasets separately using a two-stage and one-stage regression technique. These IPEs are developed for a first- and second-order relation with respect to earthquake magnitude. A "maximum intensity vs. magnitude approximation of the IPE" approach relying on an optimal hypocentral depth has also been proposed to select the best-suited IPEs. The information-theoretic approach-based Log-likelihood method (Scherbaum et al. 2009) has been used to check and compare developed IPE performance for events not used for IPE development. These newly developed equations can be used to assess the damage potential of future earthquakes. [ABSTRACT FROM AUTHOR]

Background: Methods for evaluating the strength of the knee extensor muscles play a vital role in determining the functionality of the lower limbs and monitoring any alterations that occur over time in older individuals. This study assessed the validity of the Modified Squat Test (MST) in predicting knee extensor muscle strength in older adults. Methods: This study included a total of 110 older adults. We collected demographic information such as sex, age, body weight, height, and thigh circumference. Muscle strength was assessed by measuring the maximum voluntary isometric contraction of the knee extensors, and by performing the MST (5 and 10 repetitions) and single-leg standing balance test. Stepwise multiple linear regression analysis was used to investigate multiple factors impacting the prediction of knee extensor strength. Results: Factors such as age, sex, thigh circumference, performance on the single-leg standing eye-open (SSEO) task, and the time required to complete the 10 MST repetitions together explained 77.8% of the variation in knee extensor muscle strength among older adults. We further developed a predictive equation to calculate strength as follows: strength = 36.78 - 0.24 (age) + 6.16 (sex) + 0.19 (thigh circumference) + 0.05 (SSEO) - 0.54 (time required to complete 10 MST repetitions) ± 5.51 kg. Conclusion: The 10-repetition MST is an invaluable instrument for establishing an equation to accurately predict lower limb muscle strength. [ABSTRACT FROM AUTHOR]

Background: The nasal cannula is widely regarded as a safe and effective means of administering low- and high-flow oxygen to patients irrespective of their age. However, variability in delivered oxygen concentration (FDO2 FDO2 ) via nasal cannula has the potential to pose health risks. The present study aimed to evaluate predictive equations for FDO2 over a large parameter space, including variation in breathing, oxygen flow, and upper-airway geometry representative of both young children and adults. Methods: Realistic nasal airway geometries were previously collected from medical scans of adults, infants, and neonates. Nasal airway replicas based on these geometries were used to measure the FDO2 for low-flow oxygen delivery during simulated spontaneous breathing. The present study extends previously published data sets to include higher oxygen flows. The extended data sets included nasal cannula oxygen flows that ranged from 6 to 65 L/min for the adult replicas, and from 0.5 to 6 L/min for the infant replicas. For both age groups, FDO2 was measured over a range of breathing frequencies, inspiratory to expiratory time ratios, and tidal volumes. Measured FDO2 values were compared with values predicted by using a previously derived flow-weighted equation. Results: For both age groups, FDO2 was observed to increase nonlinearly with the ratio between oxygen flow supplied to the nasal cannula and the average inhalation flow. The previously derived flow-weighted equation over-predicted FDO2 at higher oxygen flows. A new empirical equation, therefore, was proposed to predict FDO2 for either age group as a function of nasal cannula flow, tidal volume, and inspiratory time. Predicted FDO2 values matched measured values, with average relative errors of 2.4% for infants and 4.3% for adults. Conclusions: A new predictive equation for FDO2 was obtained that accurately matched measured data in both adult and infant airway replicas for low- and high-flow regimens. [ABSTRACT FROM AUTHOR]

This study aimed to develop a novel predictive equation for calculating resting metabolic rate (RMR) in women with lipedema. We recruited 119 women diagnosed with lipedema from the Angiology Outpatient Clinic at Wroclaw Medical University, Poland. RMR was assessed using indirect calorimetry, while body composition and anthropometric measurements were conducted using standardized protocols. Due to multicollinearity among predictors, classical multiple regression was deemed inadequate for developing the new equation. Therefore, we employed machine learning techniques, utilizing principal component analysis (PCA) for dimensionality reduction and predictor selection. Regression models, including support vector regression (SVR), random forest regression (RFR), and k-nearest neighbor (kNN) were evaluated in Python's scikit-learn framework, with hyperparameter tuning via GridSearchCV. Model performance was assessed through mean absolute percentage error (MAPE) and cross-validation, complemented by Bland–Altman plots for method comparison. A novel equation incorporating body composition parameters was developed, addressing a gap in accurate RMR prediction methods. By incorporating measurements of body circumference and body composition parameters alongside traditional predictors, the model's accuracy was improved. The segmented regression model outperformed others, achieving an MAPE of 10.78%. The proposed predictive equation for RMR offers a practical tool for personalized treatment planning in patients with lipedema. [ABSTRACT FROM AUTHOR]

One of the methods used to discover the development of deterioration in bricks used as a construction material in a building is the monitoring of the bricks' strength characteristics over time. However, measuring the strength characteristics of bricks used in a building requires sampling for performing laboratory tests, which is not possible in some cases. As an alternative, ultrasonic wave velocity can be a useful, nondestructive tool for the indirect assessment of the strength characteristics of the bricks. In the present study, six different samples of clay bricks before utilization as construction materials in buildings located in Khorramabad City (Lorestan Province, western Iran) were collected. The mineralogical composition of the samples was studied using X-ray diffraction (XRD) analysis. As one common physical characteristic of the construction materials, the porosity (n) of the samples was measured. Next, the strength characteristics, including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and P-wave velocity (Vp), of the samples under dry and saturated conditions were determined. It was found that after the saturation of the samples, considerable decreases in the UCS and BTS and increases in the Vp occurred, respectively. By comparing the values of the UCS, BTS, and Vp of the samples under dry and saturated conditions, we found that the integrity loss for the UCS and BTS was higher than for the Vp. Results showed that the integrity loss of the UCS, BTS, and Vp was significantly affected by the n and clay mineral (CM) content of the samples. Considering the dry or saturated condition of the samples, there are good correlations with acceptable accuracy levels between the Vp and the UCS and BTS, with coefficients of determination (R2) varying from 0.95 to 0.98. Consequently, our findings showed that establishing UCS and BTS predictive equations for bricks before their use as a construction material can be a worthy, practical tool for monitoring the deterioration of bricks over time after their utilization in a building. [ABSTRACT FROM AUTHOR]

Approximately 700,000 people in the USA have chronic kidney disease requiring dialysis. Protein-energy wasting (PEW), a condition of advanced catabolism, contributes to three-year survival rates of 50%. PEW occurs at all levels of Body Mass Index (BMI) but is devastating for those people at the extremes. Treatment for PEW depends on an accurate understanding of energy expenditure. Previous research established that current methods of identifying PEW and assessing adequate treatments are imprecise. This includes disease-specific equations for estimated resting energy expenditure (eREE). In this study, we applied machine learning (ML) modelling techniques to a clinical database of dialysis patients. We assessed the precision of the ML algorithms relative to the best-performing traditional equation, the MHDE. This was a secondary analysis of the Rutgers Nutrition and Kidney Database. To build the ML models we divided the population into test and validation sets. Eleven ML models were run and optimized, with the best three selected by the lowest root mean squared error (RMSE) from measured REE. Values for eREE were generated for each ML model and for the MHDE. We compared precision using Bland-Altman plots. Individuals were 41.4% female and 82.0% African American. The mean age was 56.4 ± 11.1 years, and the median BMI was 28.8 (IQR = 24.8 − 34.0) kg/m2. The best ML models were SVR, Linear Regression and Elastic net with RMSE of 103.6 kcal, 119.0 kcal and 121.1 kcal respectively. The SVR demonstrated the greatest precision, with 91.2% of values falling within acceptable limits. This compared to 47.1% for the MHDE. The models using non-linear techniques were precise across extremes of BMI. ML improves precision in calculating eREE for dialysis patients, including those most vulnerable for PEW. Further development for clinical use is a priority. Potentially impacting millions of patients worldwide, our continuing goal is to understand energy expenditure (EE) across the spectrum of CKD (stages 1–5) in adults and children being treated with dialysis or transplantation, with the intent of providing tools for the health professional that will improve the delivery of quality care. In past research, we have identified and focused on disease-specific variables which account for 60% of the variance in predicting EE in individuals receiving dialysis, but many questions remain unanswered. Our hypotheses are that (1) there are determinants of EE specific to CKD and, (2) predicting EE for individuals may be greatly advanced using sophisticated models that combine these determinants. In this study, we applied machine learning (ML) with linear and non-linear techniques to our existing dataset. The best models demonstrated improved precision in predicting EE for all individuals in the validation group. [ABSTRACT FROM AUTHOR]

The paper proposes a new equation for the prediction of punching shear strength of reinforced concrete flat slabs. The basis of the new predictive equation is a detailed numerical parametric study conducted using the nonlinear 3D finite element analysis using FE software MASA. For this, results of the previously tested flat slabs from literature are used as reference for validation of the numerical model. The numerical modelling procedure is validated with two previously tested slabs, one failing in pure punching prior to yielding of flexural rebar, and the second failing in flexure-punching which resulted in yielding followed by punching. The result shows that the load-displacement behavior, failure modes and the crack pattern are captured well by the analysis. Following the validation, a detailed parametric study is performed to investigate the influence of slab depth, concrete strength, longitudinal reinforcement ratio, column size and effect of reinforcement spacing. From the evaluation of results, it is observed that the punching resistance increases with slab depth but at a decreasing rate (size effect). The punching shear strength also increases with increasing longitudinal reinforcement ratio, concrete strength as well as the column area. All the reinforcing bars placed within a distance of 3.5 times the effective depth of the slab from the column center contributes significantly towards dowel action. With increasing column size, the deformation at the peak load also increases. Based on the evaluation of the results of the analyses, an empirical equation for the prediction of punching shear is derived. The results of the equation are compared with the results of a large experimental database of 235 tests, and it is shown that the proposed equation leads to better agreement with the test results compared to the equations given in the current codes (ACI, Canadian, Eurocode, Japanese code). The comparison shows that generally the predictions by existing equations in the codes tend to be unconservative for large slab with low reinforcement ratio.

Abstract Background Accurate estimation of resting energy expenditure (REE) in children and adolescents is important to establish estimated energy requirements. The objective of this study was to assess the validity of existing equations in literature and a newly developed equation in estimating REE in children and adolescents. Methods 275 participants (148 boys, 127 girls) aged 6–18 years included in the study were classified as normal-weighted, overweight, obese based on BMI z-scores for age according to WHO-2007 growth curves for 5–19 years of age. REEs were measured using an indirect calorimeter, with various equations, and a newly established equation [REE = 505.412+(24.383*FFM);Adjusted R2 = 0.649] were compared with REE measured using Bland-Altman and further validation parameters. Results When the predicted REEs were compared with the measured REEs, the highest prediction accuracy was achieved using the new Eq. (64.8%) and IOM (63.8%) for normal-weight participants, Müller FFM and new Eq. (59.6%) for overweight participants and Lazzer (44.9%) for obese participants. In normal and overweight participants, lowest root mean squared error (RMSE) values were acquired from Schmelzle’s equation (respectively 136.2;159.9 kcal/d), and the highest values were found in Kim’s Eq. (315.2; 295.2 kcal/d respectively). RMSE value of the new equation was 174.7 kcal/d for normal-weight children and adolescents, and 201.9 kcal/d for overweight ones. In obese participants, the lowest RMSE value was obtained from Schmelzle’s Eq. (305.4 kcal/d) and the new Eq. (317.4 kcal/d), while the highest value was obtained from IOM Eq. (439.9 kcal/d). RMSE was higher in obese groups compared to the other BMI groups. Conclusion Indirect-calorimeter is the most suitable method for REE measurement in especially obese children and adolescents. The new equation and Schmelzle’s equation appear to be most accurate equations for normal and overweight children and adolescents.

AbstractPurpose Approximately 700,000 people in the USA have chronic kidney disease requiring dialysis. Protein-energy wasting (PEW), a condition of advanced catabolism, contributes to three-year survival rates of 50%. PEW occurs at all levels of Body Mass Index (BMI) but is devastating for those people at the extremes. Treatment for PEW depends on an accurate understanding of energy expenditure. Previous research established that current methods of identifying PEW and assessing adequate treatments are imprecise. This includes disease-specific equations for estimated resting energy expenditure (eREE). In this study, we applied machine learning (ML) modelling techniques to a clinical database of dialysis patients. We assessed the precision of the ML algorithms relative to the best-performing traditional equation, the MHDE.Methods This was a secondary analysis of the Rutgers Nutrition and Kidney Database. To build the ML models we divided the population into test and validation sets. Eleven ML models were run and optimized, with the best three selected by the lowest root mean squared error (RMSE) from measured REE. Values for eREE were generated for each ML model and for the MHDE. We compared precision using Bland-Altman plots.Results Individuals were 41.4% female and 82.0% African American. The mean age was 56.4 ± 11.1 years, and the median BMI was 28.8 (IQR = 24.8 − 34.0) kg/m2. The best ML models were SVR, Linear Regression and Elastic net with RMSE of 103.6 kcal, 119.0 kcal and 121.1 kcal respectively. The SVR demonstrated the greatest precision, with 91.2% of values falling within acceptable limits. This compared to 47.1% for the MHDE. The models using non-linear techniques were precise across extremes of BMI.Conclusion ML improves precision in calculating eREE for dialysis patients, including those most vulnerable for PEW. Further development for clinical use is a priority.

This study aimed to develop a predictive equation for basal metabolic rate (BMR) in normal-weight Chinese adults and provide a reference for establishing the national recommended dietary energy intake. A new equation for BMR was derived from a sample of 516 normal-weight Chinese adults (men = 253, women = 263), and this sample was collected from two previous studies. Furthermore, the accuracy of this new equation and eight other previous predictive equations was reviewed. The agreement and reliability were compared in terms of bias, accuracy, the intraclass correlation coefficient, and Bland–Altman plots between predictive equations. In addition, the newly developed equation was further verified using a small independent sample, which contained 41 healthy Chinese adults (men = 21, women = 20). The measured BMR (mBMR) of all participants, measured using indirect calorimetry, was 1346.2 ± 358.0 kcal/d. Thirty participants were excluded based on Cook's distance criteria (Cook's distance of ≥0.008). Previous equations developed by Henry, Schofield, Harris–Benedict (H-B), Yang, and Hong overestimated the BMR of healthy Chinese adults. The present equation displayed the smallest average bias (0.2 kcal/d) between the mBMR and predicted basal metabolic rate (pBMR). The limits of agreement of the present equation from Bland–Altman plots were −514.3 kcal/d and 513.9 kcal/d, which is the most narrow and balanced limit of agreement. Moreover, in the verification of the testing database, the pBMR of the new equation was not significantly different from the mBMR, and the accuracy was 75.6%. Compared with pre-existing equations, the present equation is more applicable to the prediction of BMR in healthy Chinese adults. However, further studies are required to verify the accuracy of this new equation. [ABSTRACT FROM AUTHOR]

Several empirical models for the prediction of ground motion duration were developed across the world, but no model has been generated for the Himalayan region in the past. In this study, an attempt is made to study the duration models developed for different regions and compare them with a reference model developed for the Himalayan region for a wide range of magnitudes. The comparison is performed using the log-likelihood method and aims to identify the best duration prediction models based on the developed by Bajaj and Anbazhagan (2019) for the study region. The data support index values along with the weights of the corresponding models across the different distances and magnitude ranges have also been estimated. The study found that the predictive duration relation given by Lee and Green (2014) for Western North America is suitable for M ≤ 5, while the model developed by Ghanat (2011) is suitable for M > 5 for the Himalayan region. The model developed by Afshari and Stewart (2016) is also very close to the reference model. It is always preferable to have a single duration predictive model for a wide range of magnitude and distance range; hence, there is a need to develop a region-specific duration predictive model for the Himalayan region. [ABSTRACT FROM AUTHOR]

This study aimed to develop a novel predictive equation for calculating resting metabolic rate (RMR) in women with lipedema. We recruited 119 women diagnosed with lipedema from the Angiology Outpatient Clinic at Wroclaw Medical University, Poland. RMR was assessed using indirect calorimetry, while body composition and anthropometric measurements were conducted using standardized protocols. Due to multicollinearity among predictors, classical multiple regression was deemed inadequate for developing the new equation. Therefore, we employed machine learning techniques, utilizing principal component analysis (PCA) for dimensionality reduction and predictor selection. Regression models, including support vector regression (SVR), random forest regression (RFR), and k-nearest neighbor (kNN) were evaluated in Python’s scikit-learn framework, with hyperparameter tuning via GridSearchCV. Model performance was assessed through mean absolute percentage error (MAPE) and cross-validation, complemented by Bland–Altman plots for method comparison. A novel equation incorporating body composition parameters was developed, addressing a gap in accurate RMR prediction methods. By incorporating measurements of body circumference and body composition parameters alongside traditional predictors, the model’s accuracy was improved. The segmented regression model outperformed others, achieving an MAPE of 10.78%. The proposed predictive equation for RMR offers a practical tool for personalized treatment planning in patients with lipedema.

One of the methods used to discover the development of deterioration in bricks used as a construction material in a building is the monitoring of the bricks’ strength characteristics over time. However, measuring the strength characteristics of bricks used in a building requires sampling for performing laboratory tests, which is not possible in some cases. As an alternative, ultrasonic wave velocity can be a useful, nondestructive tool for the indirect assessment of the strength characteristics of the bricks. In the present study, six different samples of clay bricks before utilization as construction materials in buildings located in Khorramabad City (Lorestan Province, western Iran) were collected. The mineralogical composition of the samples was studied using X-ray diffraction (XRD) analysis. As one common physical characteristic of the construction materials, the porosity (n) of the samples was measured. Next, the strength characteristics, including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and P-wave velocity (Vp), of the samples under dry and saturated conditions were determined. It was found that after the saturation of the samples, considerable decreases in the UCS and BTS and increases in the Vp occurred, respectively. By comparing the values of the UCS, BTS, and Vp of the samples under dry and saturated conditions, we found that the integrity loss for the UCS and BTS was higher than for the Vp. Results showed that the integrity loss of the UCS, BTS, and Vp was significantly affected by the n and clay mineral (CM) content of the samples. Considering the dry or saturated condition of the samples, there are good correlations with acceptable accuracy levels between the Vp and the UCS and BTS, with coefficients of determination (R2) varying from 0.95 to 0.98. Consequently, our findings showed that establishing UCS and BTS predictive equations for bricks before their use as a construction material can be a worthy, practical tool for monitoring the deterioration of bricks over time after their utilization in a building.

Background: Accurate estimation of resting energy expenditure (REE) in children and adolescents is important to establish estimated energy requirements. The objective of this study was to assess the validity of existing equations in literature and a newly developed equation in estimating REE in children and adolescents. Methods: 275 participants (148 boys, 127 girls) aged 6–18 years included in the study were classified as normal-weighted, overweight, obese based on BMI z-scores for age according to WHO-2007 growth curves for 5–19 years of age. REEs were measured using an indirect calorimeter, with various equations, and a newly established equation [REE = 505.412+(24.383*FFM);Adjusted R2 = 0.649] were compared with REE measured using Bland-Altman and further validation parameters. Results: When the predicted REEs were compared with the measured REEs, the highest prediction accuracy was achieved using the new Eq. (64.8%) and IOM (63.8%) for normal-weight participants, Müller FFM and new Eq. (59.6%) for overweight participants and Lazzer (44.9%) for obese participants. In normal and overweight participants, lowest root mean squared error (RMSE) values were acquired from Schmelzle's equation (respectively 136.2;159.9 kcal/d), and the highest values were found in Kim's Eq. (315.2; 295.2 kcal/d respectively). RMSE value of the new equation was 174.7 kcal/d for normal-weight children and adolescents, and 201.9 kcal/d for overweight ones. In obese participants, the lowest RMSE value was obtained from Schmelzle's Eq. (305.4 kcal/d) and the new Eq. (317.4 kcal/d), while the highest value was obtained from IOM Eq. (439.9 kcal/d). RMSE was higher in obese groups compared to the other BMI groups. Conclusion: Indirect-calorimeter is the most suitable method for REE measurement in especially obese children and adolescents. The new equation and Schmelzle's equation appear to be most accurate equations for normal and overweight children and adolescents. [ABSTRACT FROM AUTHOR]

The Honeycomb Selection Design (HSD) is an innovative experimental method whose main feature is the even and systematic entry arrangement. Its systematicity, if combined with the absence of inter-plant competition that maximizes the phenotypic expression and differentiation of individual plants, enables the implementation of single-plant selection as early as the initial generations of genetic segregation, facilitating plant breeders to identify superior genotypes. Due to the specificity of entry allocation and the complexity of statistical data analysis, a specialized software becomes necessary. This article provides a detailed presentation of the 'rhoneycomb', a free and open-source R package concerning the construction, visualization, and analysis of HSDs. [ABSTRACT FROM AUTHOR]

Upper-body power, the product of muscular strength and movement velocity, is a core measure used by clinicians and researchers alike, to assess functional independence in older persons. Nevertheless, the evaluation of upper-body power is often impractical due to prohibitive cost, as well as space, equipment, and technical demands. Therefore, this study was designed to establish a predictive equation for upper-body peak power (PPUB) using the seated medicine ball throw (SMBT). Thirty-five older women (75.2 ± 6.4y) completed the SMBT and pneumatic resistance chest press peak power (CPPP) testing. A stepwise multiple regression was used to produce a predictive model using CPPP to assess PPUB. The model included SMBT distance (SMBTD) and medicine ball mass (MBMass) as significant predictors (PPUB[W] = 96.719-SMBTD [m] + 37.164-MBMass [kg]-104.499; r = 0.752; SEE = 41.41 W; p < 0.001). The performance of this model was confirmed through a cross-validation analysis using 70% (n = 23; r = 0.709; p < 0.001) and 30% (n = 12; r = 0.823; p < 0.001) of the sample. Reliability was confirmed using intraclass correlation, coefficient of variation, standard error of measurement, and Bland-Altman analyses for both MBMass. This model provides an easily accessible and reliable tool, using SMBTD and MBMass, for predicting PPUB in older women as a marker of quality of life, functional independence, and ability to complete activities of daily living. [ABSTRACT FROM AUTHOR]

A new empirical equation for free alternate bar length at the fully developed state is introduced. The equation is developed on the basis of dimensional analysis and all readily available data. A special effort is also made to align the formulation with findings from existing theoretical and numerical analyses of the development of alternate bars, and more specifically those resulting from a stability analysis of the phenomenon. When applied to the existing laboratory data, the equation results in significantly less dispersion around the best agreement line than that associated with previous empirical equations, yielding 70% of the data within the 20% error range, against 47% for previous equations. It is shown that previous equations perform notoriously poorly in the case of fully rough subcritical flows and all transitionally rough flows, with the present equation addressing this matter. When applied to field cases, the present equation yielded realistic values of bar length, including for large sand streams with high or very high values of relative depth (e.g., the Tagus and Mississippi Rivers). [ABSTRACT FROM AUTHOR]

Resting energy expenditure (REE) is often estimated in athletes using equations developed from the general population however, the application in athletic-specific populations is questionable. The aim of this systematic review was to compare measured REE and estimations of REE obtained from non-sport participants and athletes. Inclusion criteria met PICO criteria: population -- participants involved in organized sport; intervention -- resting energy expenditure was obtained by calorimetry; comparator -- equations to estimate REE; outcomes -- comparisons between measured REE and predicted REE. The search was conducted in Web of Science all databases, PubMed and Scopus. Comparisons between measured REE and predicted REE as well the potential models to estimate REE developed among athletes were summarized. Allowing for variation among studies, equations developed within general populations were not comparable to REE measured by calorimetry in athletes. Equations across athletic samples were obtained but, few studies tested their validity across independent samples of sport participants. Nevertheless, equations developed within athlete populations seem to be widely unused in sports nutrition literature and practice. De Lorenzo and ten Haaf equations appear to present an acceptable agreement with measured REE. Finally, equations used among adults should not be generalised for youth sport participants. [ABSTRACT FROM AUTHOR]

Purpose For individuals receiving maintenance dialysis, estimating accurate resting energy expenditure (REE) is essential for achieving energy balance, and preventing protein-energy wasting. Dialysis-specific, predictive energy equations (PEEs) offer a practical way to calculate REE. Three PEEs have been formulated via similar methods in different demographic samples; the Maintenance Haemodialysis Equation (MHDE REE), Vilar et al. Equation (Vilar REE) and the Fernandes et al. Equation (Cuppari REE). We compared them in a US cohort and assessed precision relative to measured REE (mREE) from indirect calorimetry. Because of expected imprecision at the extremes of the weight distribution, we also assessed the PEEs stratified by body mass index (BMI) subgroups.Methods This analysis comprised of 113 individuals from the Rutgers Nutrition and Kidney Database. Estimated REE (eREE) was calculated for each PEE, and agreement with mREE was set at > 50% of values within the limits of ±10%. Reliability and accuracy were determined using intraclass correlation (ICC) and a Bland Altman plot, which analysed the percentage difference of eREE form mREE.Results Participants were 58.4% male and 81.4% African American. Mean age was 55.8 ± 12.2 years, and the median BMI was 28.9 (IQR = 25.3 − 34.4) kg/m2. The MHDE REE achieved 58.4% of values within ±10% from mREE; Cuppari REE achieved 47.8% and Vilar REE achieved 46.0% agreement. Reliability was good for the MHDE REE (ICC = 0.826) and Cuppari REE (ICC = 0.801), and moderate for the Vilar REE (ICC = 0.642) (p

Resting energy expenditure (REE) is often estimated in athletes using equations developed from the general population however, the application in athletic-specific populations is questionable. The aim of this systematic review was to compare measured REE and estimations of REE obtained from non-sport participants and athletes. Inclusion criteria met PICO criteria: population – participantsinvolved in organized sport; intervention – resting energy expenditure was obtained by calorimetry; comparator – equations to estimate REE; outcomes – comparisons between measured REE and predicted REE. The search was conducted in Web of Science all databases, PubMed and Scopus. Comparisons between measured REE and predicted REE as well the potential models to estimate REE developed among athletes were summarized. Allowing for variation among studies, equations developed within general populations were not comparable to REE measured by calorimetry in athletes. Equations across athleticsamples were obtained but, few studiestested their validity acrossindependent samples of sport participants. Nevertheless, equations developed within athlete populations seem to be widely unused in sports nutrition literature and practice. De Lorenzo and ten Haaf equations appear to present an acceptable agreement with measured REE. Finally, equations used among adults should not be generalised for youth sport participants.

Introduction: There is no global reference value for the 6-minute walking distance (6MWD) in paediatric populations, as it can vary greatly depending on local characteristics and anthropometric measures. This study aimed to identify a 6MWD reference value that could be applied in both local and regional settings. Methods: This cross-sectional multicentre study investigated a healthy paediatric population aged 4–18 years in Indonesia. The 6-minute walk test (6MWT) was conducted in accordance with the American Thoracic Society guidelines. Data were presented as the 6MWD according to age and sex per year. Univariate and multivariate analyses were conducted on the basis of the 6MWDpred Rizky formula. Results: A total of 634 participants were included in this study. Age, sex, weight, leg length and height affected the 6MWD (P<0.001). In the regression model, sex and height were the predictors of 6MWD, with height as the best single predictor. Conclusion: The reference charts and 6MWDpred Rizky formula are applicable in multi-ethnic paediatric Indonesian populations but in limited settings. [ABSTRACT FROM AUTHOR]

For individuals receiving maintenance dialysis, estimating accurate resting energy expenditure (REE) is essential for achieving energy balance, and preventing protein-energy wasting. Dialysis-specific, predictive energy equations (PEEs) offer a practical way to calculate REE. Three PEEs have been formulated via similar methods in different demographic samples; the Maintenance Haemodialysis Equation (MHDE REE), Vilar et al. Equation (Vilar REE) and the Fernandes et al. Equation (Cuppari REE). We compared them in a US cohort and assessed precision relative to measured REE (mREE) from indirect calorimetry. Because of expected imprecision at the extremes of the weight distribution, we also assessed the PEEs stratified by body mass index (BMI) subgroups. This analysis comprised of 113 individuals from the Rutgers Nutrition and Kidney Database. Estimated REE (eREE) was calculated for each PEE, and agreement with mREE was set at > 50% of values within the limits of ±10%. Reliability and accuracy were determined using intraclass correlation (ICC) and a Bland Altman plot, which analysed the percentage difference of eREE form mREE. Participants were 58.4% male and 81.4% African American. Mean age was 55.8 ± 12.2 years, and the median BMI was 28.9 (IQR = 25.3 − 34.4) kg/m2. The MHDE REE achieved 58.4% of values within ±10% from mREE; Cuppari REE achieved 47.8% and Vilar REE achieved 46.0% agreement. Reliability was good for the MHDE REE (ICC = 0.826) and Cuppari REE (ICC = 0.801), and moderate for the Vilar REE (ICC = 0.642) (p <.001 for all). The equations performed poorly at the lowest and highest BMI categories. Dialysis-specific energy equations showed variable accuracy. When categorized by BMI, the equations performed poorly at the extremes, where individuals are most vulnerable. Innovation is needed to understand these variances and correct the imprecision in PEEs for clinical practice. Potentially impacting over millions of patients worldwide, our long-term goal is to understand energy expenditure (EE) across the spectrum of CKD (stages 1–5) in adults and children being treated with dialysis or transplantation, with the intent of providing tools for the health professional that will improve the delivery of quality care. Our research has identified and focussed on disease-specific factors which account for 60% of the variance in predicting EE in patients on MHD, but significant gaps remain. Thus, our central hypotheses are that (1) there are unique disease-specific determinants of EE and (2) prediction of EE for individuals diagnosed with CKD can be vastly improved with a model that combines these factors with more sophisticated approaches. [ABSTRACT FROM AUTHOR]

This study aimed to develop a predictive equation for basal metabolic rate (BMR) in normal-weight Chinese adults and provide a reference for establishing the national recommended dietary energy intake. A new equation for BMR was derived from a sample of 516 normal-weight Chinese adults (men = 253, women = 263), and this sample was collected from two previous studies. Furthermore, the accuracy of this new equation and eight other previous predictive equations was reviewed. The agreement and reliability were compared in terms of bias, accuracy, the intraclass correlation coefficient, and Bland–Altman plots between predictive equations. In addition, the newly developed equation was further verified using a small independent sample, which contained 41 healthy Chinese adults (men = 21, women = 20). The measured BMR (mBMR) of all participants, measured using indirect calorimetry, was 1346.2 ± 358.0 kcal/d. Thirty participants were excluded based on Cook’s distance criteria (Cook’s distance of ≥0.008). Previous equations developed by Henry, Schofield, Harris–Benedict (H-B), Yang, and Hong overestimated the BMR of healthy Chinese adults. The present equation displayed the smallest average bias (0.2 kcal/d) between the mBMR and predicted basal metabolic rate (pBMR). The limits of agreement of the present equation from Bland–Altman plots were −514.3 kcal/d and 513.9 kcal/d, which is the most narrow and balanced limit of agreement. Moreover, in the verification of the testing database, the pBMR of the new equation was not significantly different from the mBMR, and the accuracy was 75.6%. Compared with pre-existing equations, the present equation is more applicable to the prediction of BMR in healthy Chinese adults. However, further studies are required to verify the accuracy of this new equation.

Background: Indirect calorimetry (IC) is the gold standard for measuring resting energy expenditure. Energy expenditure (EE) estimated by ventilator‐derived carbon dioxide consumption (EEVCO2) has also been proposed. In the absence of IC, predictive weight‐based equations have been recommended to estimate daily energy requirements. This study aims to compare simple predictive weight‐based equations with those estimated by EEVCO2 and IC in mechanically ventilated patients of COVID‐19. Methods: Retrospective study of a cohort of critically ill adult patients with COVID‐19 requiring mechanical ventilation and artificial nutrition to compare energy estimations by three methods through the calculation of bias and precision agreement, reliability, and accuracy rates. Results: In 58 mechanically ventilated patients, a total of 117 paired measurements were obtained. The mean estimated energy derived from weight‐based calculations was 2576 ± 469 kcal/24 h, as compared with 1507 ± 499 kcal/24 h when EE was estimated by IC, resulting in a significant bias of 1069 kcal/day (95% CI [−2158 to 18.7 kcal]; P < 0.001). Similarly, estimated mean EEVCO2 was 1388 ± 467 kcal/24 h when compared with estimation of EE from IC. A significant bias of only 118 kcal/day (95% CI [−187 to 422 kcal]; P < 0.001), compared by the Bland‐Altman plot, was noted. Conclusion: The energy estimated with EEVCO2 correlated better with IC values than energy derived from weight‐based calculations. Our data suggest that the use of simple predictive equations may potentially lead to overfeeding in mechanically ventilated patients with COVID‐19. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to develop and cross-validate a fat-free mass (FFM) predictive equation using multifrequency bioelectrical impedance analysis (BIA) data in adolescent soccer athletes., Methods: Male adolescent soccer athletes (n = 149; 13-19 y old) were randomly sorted using Excel and independently selected for development group (n = 100) or cross-validation group (n = 49). The FFM reference values were determined using dual-energy X-ray absorptiometry. Single-frequency BIA was used to plot tolerance ellipses. Multifrequency-BIA raw data were used as independent variables in regression models. Student's independent t-test was used to compare development and cross-validation groups. Stepwise multiple regression was used to develop the FFM predictive equation. Bland-Altman plots, Lin's concordance correlation coefficient, according to McBride criteria, precision, accuracy, and standard error of estimate (SEE) were calculated to evaluate the concordance and reliability of estimates. Bioelectrical impedance vector analysis was plotted to assess hydration status., Results: No differences (P > 0.05) were observed between development and validation groups in chronological age, anthropometric data, bioelectrical impedance data, and FFM values obtained using dual-energy X-ray absorptiometry. Bioelectrical impedance vector analysis tolerance showed that all participants presented adequate hydration status compared to the reference population. The new FFM predictive equation developed and validated: FFM (kg) = -7.064 + 0.592 × chronological age (y) + 0.554 × weight (kg) + 0.365 × height²/resistance (cm²/Ω), presented R² = 0.95; SEE = 1.76 kg; concordance correlation coefficient = 0.95, accuracy = 0.98, and strength of concordance = 0.99., Conclusions: The present study developed and cross-validated an FFM predictive equation based on multifrequency bioelectrical data providing substantial FFM accuracy for male adolescent soccer athletes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The composition of raw milk is of major importance for dairy products, especially fat, protein, and casein (CN) contents, which are used worldwide in breeding programs for dairy species because of their role in human nutrition and in determining cheese yield (%CY). The aim of the study was to develop formulas based on detailed milk composition to disentangle the role of each milk component on %CY traits. To this end, 1,271 individual milk samples (1.5 L/cow) from Brown Swiss cows were processed according to a laboratory model cheese-making procedure. Fresh %CY (%CYCURD), total solids and water retained in the fresh cheese (%CYSOLIDS and %CYWATER), and 60-days ripened cheese (%CYRIPENED) were the reference traits and were used as response variables. Training-testing linear regression modeling was performed: 80% of observations were randomly assigned to the training set, 20% to the validation set, and the procedure was repeated 10 times. Four groups of predictive equations were identified, in which different combinations of predictors were tested separately to predict %CY traits: (i) basic composition, i.e., fat, protein, and CN, tested individually and in combination; (ii) udder health indicators (UHI), i.e., fat + protein or CN + lactose and/or somatic cell score (SCS); (iii) detailed protein profile, i.e., fat + protein fractions [CN fractions, whey proteins, and nonprotein nitrogen (NPN) compounds]; (iv) detailed protein profile + UHI, i.e., fat + protein fractions + NPN compounds and/or UHI. Aside from the positive effect of fat, protein, and total casein on %CY, our results allowed us to disentangle the role of each casein fraction and whey protein, confirming the central role of β-CN and κ-CN, but also showing α-lactalbumin (α-LA) to have a favorable effect, and β-lactoglobulin (β-LG) a negative effect. Replacing protein or casein with individual milk protein and NPN fractions in the statistical models appreciably increased the validation accuracy of the equations. The cheese industry would benefit from an improvement, through genetic selection, of traits related to cheese yield and this study offers new insights into the quantification of the influence of milk components in composite selection indices with the aim of directly enhancing cheese production.

The Honeycomb Selection Design (HSD) is an innovative experimental method whose main feature is the even and systematic entry arrangement. Its systematicity, if combined with the absence of inter-plant competition that maximizes the phenotypic expression and differentiation of individual plants, enables the implementation of single-plant selection as early as the initial generations of genetic segregation, facilitating plant breeders to identify superior genotypes. Due to the specificity of entry allocation and the complexity of statistical data analysis, a specialized software becomes necessary. This article provides a detailed presentation of the ‘rhoneycomb’, a free and open-source R package concerning the construction, visualization, and analysis of HSDs.

Cross-laminated timber (CLT) as one novel engineered massive wood is prone to the rolling shear failure, due to its configuration characteristics of orthogonal orientation of adjacent layers. For comprehending its rolling shear behavior and clarifying the influence of the lamination aspect ratios on the rolling shear strength, pseudo-static monotonic rolling shear tests were conducted on the CLT specimens with aspect ratios ranging from 2.54 to 9.40, based on a modified planar shear test method. Their rolling shear strength or rolling shear resisting capacity was calculated with respect to the influence of the aspect ratios. The damage modes of the rolling shear specimens were analyzed considering the influence of their lamination width. The effect of the lamination width and that of the lamination thickness on the rolling shear strength were investigated, respectively. Besides, in the case of different aspect ratios, the strength modification factor defined as the ratio between the design rolling shear strength to the design parallel-to-grain shear strength of the outermost laminations was provided, which can facilitate the estimation of the rolling shear strength. Considering the influence of the aspect ratios, both the regression equations of the strength modification factor and the predictive equations of the rolling shear strength were proposed. It is found that a highly positive linear correlation exists between the rolling shear resisting capacity and the aspect ratio. The damage modes of the CLT rolling shear specimens depend on their lamination width; besides, when the lamination width increases from 184 to 235 mm, little improvement can be identified for the rolling shear resisting capacity. Meanwhile, larger thickness of the CLT transverse laminations can result in less CLT rolling shear strength. Overall, the proposed equations are capable of predicting the rolling shear strength of CLT fabricated with the SPF lumber. The study can contribute to the comprehension on the CLT rolling shear behaviors and provide reference values for mitigating the possibilities of CLT rolling shear damages in engineering design. [ABSTRACT FROM AUTHOR]

Background: Weight homeostasis is complex in Parkinson's disease (PD) and body weight changes substantially throughout the course of the disease. We designed a case–control study to (i) investigate whether PD is associated with changes in resting energy expenditure (REE), (ii) to assess how accurately REE could be predicted for individuals with PD utilizing the equations constructed for healthy individuals, and (iii) to eventually construct a new equation. Materials & Methods: Measured REE (mREE) was compared between 122 PD patients and 122 gender and body mass index (BMI)-matched controls. The accuracy of estimated REE by 5 common equations (Harris/Benedict-1919, Roza/Shizgal-1984, Mifflin St. Jeor, WHO/FAO and aggregate formula) was investigated in PD using Bland–Altman analysis and reported as the frequency of accurate predictions (±10%). Concordance correlation coefficients (CCC) were also calculated. Then, we regressed a new REE equation – using gender, age, weight, height and Hoehn-Yahr stage – and validated it in an independent sample (N = 100). Results: No significant difference in mREE was recorded between the whole PD sample and healthy controls. However, mREE was increased in patients with BMI ≥ 30 kg/m2 and Hoehn-Yahr stage ≥ 3. Limited accuracy was present in the available REE equations (accurate prediction [±10%] frequency, <60% for all). For the new equation, the proportion of accurate prediction was 67.0% (overestimation, 24.0%) and CCC was 0.77. Conclusion: PD patients are not commonly characterized by an increase in REE. This is limited to patients suffering from obesity and more severe disease. Common REE equations appear to be inaccurate. The new predictive equation proposed in this study provided better REE estimates. [ABSTRACT FROM AUTHOR]

Massive landslides can generate large tsunamis and endanger human lives and properties. While a number of laboratory experiments on either subaerial or submarine landslide‐tsunamis have been conducted, few studies have focused on partially submerged events. The present study aimed to investigate the characteristics of partially submerged landslide‐tsunamis and to develop a predictive equation that can estimate their heights through three‐dimensional laboratory experiments that used 114 different conditions. The experimental results showed that, while waves generated in the landslide sliding direction had around 1.7 times higher amplitude than those propagating laterally in the near‐field region, the wave period, celerity, and wavelength did not alter significantly according to the propagation angles. The effects of wave dispersion were also shown to be significant during the propagation process. Such wave characteristics were consistent with those reported in existing subaerial landslide tsunami experiments conducted by other researchers. Based on the data set obtained, the authors also derived a predictive equation which was confirmed to be able to estimate the results of other experimental studies on partially submerged landslide‐tsunamis with an error of around ±30%, and thus can be helpful for a rapid tsunami hazard assessment. The developed equation indicated that the volume of a landslide could have more than twice the impact on the height of a partially submerged landslide‐tsunami than other parameters. The effects of the landslide size were also shown to be more significant than in the case of a subaerial landslide‐tsunami, though the wave attenuation process was similar in both types. Plain Language Summary: Massive landslides can generate large tsunamis and endanger human lives and properties. While many laboratory experiments on landslide‐generated tsunamis have been conducted, few studies have investigated on partially submerged landslide‐tsunamis. The present study conducted three‐dimensional laboratory experiments on partially submerged landslide‐tsunamis to investigate the characteristics of such waves and to develop a predictive equation of their likely height. The results showed that waves generated in the landslide sliding direction were higher than those generated in the lateral direction, though other wave properties did not change greatly (which is similar to that reported by other researchers for other types of landslide tsunamis). In addition, the results show that partially submerged landslide tsunamis have similar wave propagation properties as those of other types of landslide tsunamis. Based on the experimental results, the authors also derived a predictive equation and confirmed that it can accurately estimate the heights of partially submerged landslide‐tsunamis. The derived equation can be helpful when conducting a rapid tsunami hazard assessment. It was also found that whereas the generation process of a partially submerged landslide‐tsunami is different from a subaerial landslide‐tsunami, the propagation process is similar in both types. Key Points: Three‐dimensional laboratory experiments were conducted on partially submerged landslide‐tsunamisWhile the amplitudes of the generated waves differed according to the propagation angles, other wave properties were not clearly differentThe derived predictive equation is able to estimate the height of the partially submerged landslide‐tsunamis well [ABSTRACT FROM AUTHOR]

Abstract Background We aimed to investigate the usefulness of magnetic resonance imaging (MRI) and histopathological shrinkage patterns to formulate a predictive equation for estimating residual tumor size after neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients. Methods We enrolled 34 TNBC patients who underwent MRI before and after NAC. The MRI and histopathological shrinkage patterns were analyzed and classified into five categories—types I and II (concentric shrinkage without or with surrounding lesions, respectively), type III (shrinkage with residual multinodular lesions), type IV (diffuse contrast enhancement in the entire quadrant), and non-visualization. The residual tumor sizes following MRI and histopathological examination were also compared. Results The most common MRI and histopathological shrinkage pattern was type I (41.2% and 38.2%, respectively), followed by non-visualization (26.5% and 32.4%, respectively); the concordance rate between MRI and histopathological shrinkage patterns was 41.2%. There was a strong correlation between MRI tumor size and pathological tumor size (r = 0.89). Based on these findings, a predictive equation for pathological tumor size was formulated as follows: pathological tumor size (mm) = 1.1502 × (MRI tumor size [mm]) + 8.4277. Conclusions Our equation may aid accurate preoperative assessment. Further studies are needed to determine its predictive value and applicability.

Background: Continuous positive airway pressure (CPAP) is the treatment of obstructive sleep apnea (OSA). The CPAP pressure is generally estimated by manual titration or an auto CPAP device. An alternative method involves the use of the predictive equation. Aim and Objective: There is no equation developed, taking into account the Indian population. The aim is to develop a predictive equation for optimal CPAP pressure in patients with OSA and to validate the equation by comparing it with manual titration pressure. Materials and Methods: A total of 250 patients with OSAS who underwent successful manual titration for CPAP treatment in a tertiary care center were included in this study and divided randomly into two groups A and B with 150 and 100 patients, respectively. Stepwise multiple regression analysis was applied to anthropometric and polysomnographic variables of group A and the predictive equation for estimating CPAP was developed using SPSS. This equation was validated by comparing the estimated pressure with that of manual titration pressure in Group B. Results: The mean age was 55.09 ± 11.43 and mean body mass index (BMI) was 33.69 ± 6.56. CPAP pressure in patients with OSA was 11.13 ± 1.83 cm H2O. The Apnea Hypopnea index (AHI) (r = 0.595, P < 0.001), minimal; SpO2 (r = −0.502, P < 0.001), BMI (r = 0.494, P < 0.001) significantly correlated with optimal CPAP level. A predictive equation for optimal CPAP level in patients with OSA was developed using AHI, BMI, and minimal SpO2, which can be easily measured during the diagnostic process. Optimal CPAP level (cm H2O) =8.401+ (0.053 × BMI) + (0.020 × AHI) − (0.031 × lowest oxygen). Twenty-six percent of the variance in the optimal CPAP level was explained by this equation (R2 = 0.26, P < 0.001) and the equation showed 86% of optimal estimation. Conclusion: The results suggest that manual titration pressure correlates with the pressure derived from the predictive equation in our study.

Introduction: There is no global reference value for the 6-minute walking distance (6MWD) in paediatric populations, as it can vary greatly depending on local characteristics and anthropometric measures. This study aimed to identify a 6MWD reference value that could be applied in both local and regional settings. Methods: This cross-sectional multicentre study investigated a healthy paediatric population aged 4–18 years in Indonesia. The 6-minute walk test (6MWT) was conducted in accordance with the American Thoracic Society guidelines. Data were presented as the 6MWD according to age and sex per year. Univariate and multivariate analyses were conducted on the basis of the 6MWDpred Rizky formula. Results: A total of 634 participants were included in this study. Age, sex, weight, leg length and height affected the 6MWD (P

Background and Aims: The determination of energy requirements is necessary to promote adequate growth and nutritional status in pediatric populations. Currently, several predictive equations have been designed and modified to estimate energy expenditure at rest. Our objectives were (1) to identify the equations designed for energy expenditure prediction and (2) to identify the anthropometric and demographic variables used in the design of the equations for pediatric patients who are healthy and have illness.Methods: A systematic search in the Medline/PubMed, EMBASE and LILACS databases for observational studies published up to January 2021 that reported the design of predictive equations to estimate basal or resting energy expenditure in pediatric populations was carried out. Studies were excluded if the study population included athletes, adult patients, or any patients taking medications that altered energy expenditure. Risk of bias was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies.Results: Of the 769 studies identified in the search, 39 met the inclusion criteria and were analyzed. Predictive equations were established for three pediatric populations: those who were healthy (n = 8), those who had overweight or obesity (n = 17), and those with a specific clinical situation (n = 14). In the healthy pediatric population, the FAO/WHO and Schofield equations had the highest R2 values, while in the population with obesity, the Molnár and Dietz equations had the highest R2 values for both boys and girls.Conclusions: Many different predictive equations for energy expenditure in pediatric patients have been published. This review is a compendium of most of these equations; this information will enable clinicians to critically evaluate their use in clinical practice.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=226270, PROSPERO [CRD42021226270].

Objective: A study was conducted to quantify the performance differences of the nearinfrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations., Methods: A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters., Results: The performance of the developed NIRS calibration models was evaluated based on R2, standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R2 (coefficient of determination), standard error, and RPD. Although the R2 of calibration models was mainly greater than 0.90 across the feed value indexes, the R2 of cross-validations was much lower. The R2 of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling., Conclusion: Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.

Objectives: This is a cross-sectional study, aimed to develop and cross-validate a fat-free mass (FFM) predictive equation using single-frequency bioelectrical impedance (BIA), considering the predicted age at peak height velocity (PHV) as a variable. Additionally, the study aims to test the FFM-BIA obtained using a previous predictive equation that used skeletal maturity as a variable., Method: The participants (n = 169 male adolescent athletes) were randomly divided into two groups: development of a new predictive equation (n = 113), and cross-validation (n = 56). The concordance test between the FFM values obtained by Koury et al. predictive equation and DXA data was determined (n = 169). Bioelectrical data was obtained using a single-frequency analyzer., Results: Among the models tested, the new predictive equation has resistance index (height2/resistance) and predictive age at PHV as variables and presented R2 = 0.918. The frequency of maturity status using skeletal maturity and PHV diagnosis was inadequate (Kappa = 0.4257; 95%CI = 0.298-0.553). Bland-Altman plots and concordance correlation coefficient showed substantial concordance between the FFM-DXA values (48.8 ± 11.2 kg) and the new predictive equation (CCC = 0.960). The results showed that the new equation performed better than the equation developed by Koury et al. (CCC = 0.901)., Conclusions: Our results show that it is feasible to predict FFM in male adolescent athletes using predictive age at PHV, with moderate concordance. The calculation of FFM using more economical and less complex variables is viable and should be further explored., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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Near-real-time, high-spatial-resolution leaf area index (LAI) maps would enable producers to monitor crop health and growth status, improving agricultural practices such as fertiliser and water management. LAI retrieval methods are numerous and can be divided into statistical and physically based methods. While statistical methods are generally subject to high site-specificity but possess high ease of implementation and use, physically based methods are more transferable, albeit more complex to use in operational settings. In addition, statistical methods need a large amount of data for calibration and subsequent validation, and this is only seldom feasible. Techniques based on predictive equations (PEphysical) represent a viable alternative, allowing the partial combination of statistical and physical methods merits while minimising their shortcomings. In this paper, predictive equation-based techniques were compared with four other methods: two radiative transfer model (RTM) inversion methods, one based on neural network (NNET) and one based on a look-up table (LUT), and two empirical methods (one using empirical models based on vegetation indices and in situ data and one based on empirical models found in the scientific literature). The methods were chosen based on common use. To evaluate the performance of the studied methods, the coefficient of determination (R2), root mean square error (RMSE), and normalised root mean square error (nRMSE, %) between the estimates and in situ LAI measurements were reported. The best PEphysical results, achieved by the OSAVI index (RMSE = 0.84 m2 m−2), provided better performance for LAI recovery than the NNET-based RTM inversions (0.86 m2 m−2) or the estimates made by LUT (0.94 m2 m−2). Furthermore, the best PEphysical produced accuracies comparable to the best empirical model (RMSE = 0.71 m2 m−2), calibrated through in situ data, and similar to the best literature model (RMSE = 0.76 m2 m−2). These results indicated that PEphysical can be used to recover LAI with transferability comparable to literature models.

Aim: The aim of this study was to investigate the changes in anterior segment parameters as assessed by ultrasound biomicroscopy (UBM) after laser peripheral iridotomy (LPI) and to propose a prediction equation for the width of the angle after LPI.Design: This was a prospective study.Participants: The participants included 100 subjects with primary angle closure suspect (PACS).Methods: Anterior segment UBM parameters were measured, whereas AOD750 was chosen to indicate the width of the angle associated with gonioscopic angle closure, as found in a prior study.Main Outcome Measures: Angle parameters, iris parameters, anterior chamber parameters and ciliary body parameters.Results: All angle parameters increased after LPI, including the mean angle opening distance at 750 μm (AOD750), mean angle opening distance at 500 μm from the scleral spur (AOD500), mean angle opening distance at 750 μm from the scleral spur (AOD750), and mean angle recess area at 750 μm from the scleral spur (ARA750). Among iris parameters and ciliary body parameters, the iris thickness at 2,000 μm (IT2000), iris curvature (IC), and trabecular-ciliary process distance (ICPD) were reduced after LPI. The final equation consisted of four parameters: anterior chamber depth (ACD), iris thickness at 750 μm from the scleral spur (IT750), AOD750, and lens vault (LV). This equation explained 42.7% of the variability in the angle opening indicator AOD750 after LPI, whereas in the plateau iris configuration subgroup, the accuracy of the prediction equation reached the highest a maximum of 68.6%.Conclusions: There was an increase in angle opening and iris flattening after LPI. An equation involving four angle parameters was constructed, this equation which could explained 42.7% of the variability in the angle opening indicator AOD750 after LPI whereas in the plateau iris configuration subgroup, the accuracy of the prediction equation reached a maximum of 68.6%.

Objective: Several predictive equations have been used to estimate patients' energy expenditure. The study aimed to describe the characteristics of resting energy expenditure (REE) in patients undergoing mechanical ventilation during early postoperative stage after cardiac surgery and evaluate the validity of 9 REE predictive equations. Methods: This was a prospective observational study. Patients aged 18–80 years old, undergone open-heart surgery, were enrolled between January 2017 and 2018. The measured REE (mREE) was evaluated via indirect calorimetry (IC). The predictive resting energy expenditure (pREE) was suggested by 9 predictive equations, including Harris-Benedict (HB), HB coefficient method, Ireton-Jones, Owen, Mifflin, Liu, 25 × body weight (BW), 30 × BW, and 35 × BW. The association between mREE and pREE was assessed by Pearson's correlation, paired t test, Bland-Altman method, and the limits of agreement (LOA). Results: mREE was related to gender, BMI, age, and body temperature. mREE was significantly correlated with pREE, as calculated by 9 equations (all p < 0.05). There was no significant difference between pREE and mREE, as calculated by 30 × BW kcal/kg/day (t = 0.782, p = 0.435), while significant differences were noted between mREE and pREE calculated by other equations (all p < 0.05). Taking the 30 × BW equation as a suitable candidate, most of the data points were within LOA, and the percentage was 95.6% (129/135). Considering the rationality of clinical use, accurate predictions (%) were calculated, and only 40.74% was acceptable. Conclusions: The 30 × BW equation is relatively acceptable for estimating REE in 9 predictive equations in the early stage after heart surgery. However, the IC method should be the first choice if it is feasible. [ABSTRACT FROM AUTHOR]

The steady state behavior of thermodynamically supercritical natural circulation loops (NCLs) is investigated in this work. Experimental steady state results with carbon dioxide are presented for supercritical pressures in the range of 80–120 bar, and temperatures in the range of 20–65 ° C. Distinct thermodynamic states are reached by traversing a set of isochors. An equation for the prediction of the steady state of NCLs at supercritical pressures is presented, and its performance is assessed using empirical data. Changes of mass flow rate as a result of independent changes of thermodynamic state, heating rate, driving height and viscous losses are shown to be accurately captured by the proposed equation. Furthermore, close agreement between the predicted and measured mass flow rate is found when the measured equipment losses are taken into account for the comparison. Subsequently, the findings are put forward in aid of the development of safe, novel supercritical natural circulation facilities. • Experimental investigation of natural circulation loop (NCL) with supercritical CO 2. • A steady-state mass flow rate equation is proposed for supercritical pressure NCLs. • An experimental sensitivity analysis substantiates the validity of the equation. • The applicability of the equation to large-scale systems is elaborated on. [ABSTRACT FROM AUTHOR]

Abstract Background Although over the years a number of studies have used chest circumference (CC) as a sensitive tool to identify the health status of infants, a particularly important aspect for this population is the lack of data on normal values and prediction equations. In order to facilitate and validate the interpretation of CC data in newborn (NB), the aim was to study the relation between CC and other anthropometric variables and develop a predictive equation for CC in a population of full-term newborns. Methods Cross-sectional study, carried out with full-term infants. The anthropometric (CC, head circumference - HC, length, age and weight) and hemodynamic variables were evaluated during the first 24 h of life. Bivariate analysis was performed between CC and HC, weight, length and type of delivery, followed by multiple linear regression analysis, including variables that were significant in the bivariate analysis. For data analysis, we used the SPSS program, considering p

Resting energy expenditure (REE) is determined mainly by fat-free mass (FFM). FFM depends also on daily physical activity. REE normally decreases with increased age due to decreases in FFM and physical activity. Measuring REE is essential for estimating total energy expenditure. As such, there are a number of different equations in use to predict REE. In recent years, an increasing number of older adults continue to participate in competitive sports creating the surge of master athletes. It is currently unclear if these equations developed primarily for the general population are also valid for highly active, older master athletes. Therefore, we tested the validity of six commonly-used equations for predicting REE in master athletes. In conjunction with the World Masters Athletic Championship in Malaga, Spain, we measured REE in 113 master athletes by indirect calorimetry. The most commonly used equations to predict REE [Harris & Benedict (H&B), World Health Organization (WHO), Müller (MÜL), Müller-FFM (MÜL-FFM), Cunningham (CUN), and De Lorenzo (LOR)] were tested for their accuracies. The influences of age, sex, height, body weight, FFM, training hours per week, phase angle, ambient temperature, and athletic specialization on REE were determined. All estimated REEs for the general population differed significantly from the measured ones (H&B, WHO, MÜL, MÜL-FFM, CUN, all p < 0.005). The equation put forward by De Lorenzo provided the most accurate prediction of REE for master athletes, closely followed by FFM-based Cunningham’s equation. The accuracy of the remaining commonly-used prediction equations to estimate REE in master athletes are less accurate. Body weight (p < 0.001), FFM (p < 0.001), FM (p = 0.007), sex (p = 0.045) and interestingly temperature (p = 0.004) are the significant predictors of REE. We conclude that REE in master athletes is primarily determined by body composition and ambient temperature. Our study provides a first estimate of energy requirements for master athletes in order to cover adequately athletes’ energy and nutrient requirements to maintain their health status and physical performance.