Simple Prediction Equation Research Articles

Valid and accurate simple equation to predict 3,000-m steeplechase performance.

Predict running performances is very important for athletes and trainers. Sport researchers have therefore developed certain tools to predict running performances, but only in non-obstacle races. This study aimed to develop and test the validity and accuracy of an equation for predicting 3,000-m steeplechase performance (PerfSteeple ). The official rankings of French runners for the 3,000-m track-running (Perf3000 ) and 3,000-m steeplechase events were examined. Age, height and body mass were collected. From 146 included athletes, two groups were randomly composed: one comprising 80% of the sample (n = 117) to develop a simple equation to predict PerfSteeple (i.e., development group) and the other comprising the remaining 20% (n = 29) to test the validity and accuracy of the developed prediction equation (i.e., cross-validation group). The simple prediction equation included Perf3000 and age: . No significant difference was noted between the actual and predicted performances. Predicted performances were significantly correlated with the actual ones, with a very high correlation coefficient (p < 0.001; r = 0.929). Bias and 95% limits of agreement were -5 ± 24 s, i.e., -0.8 ± 7.6%. In 95 of 100 new predictions, the difference between actual and predicted performance would be less or equal to-5 ± 24 s. The study confirms the validity and accuracy of the equation for predicting PerfSteeple . Predictions using this simple equation may be used in training and competitions for athletes and coaches. PerfSteeple = -57,165 + 1,147 X Perf3000 + 0,955 X age.

New Predictive Equation for the Estimation of Plasma Concentrations of Formed Colistin in Patients Treated With Colistimethate Sodium for Multidrug-Resistant Gram-Negative Bacterial Infections.

The clinical use of colistin methanesulphonate (CMS) is limited by potential nephrotoxicity. The selection of an efficient and safe CMS dose for individual patients is complicated by the narrow therapeutic window and high interpatient pharmacokinetic variability. In this study, a simple predictive equation for estimating the plasma concentration of formed colistin in patients with multidrug and extremely drug-resistant gram-negative bacterial infections was developed. The equation was derived from the largest clinical cohort of patients undergoing therapeutic drug monitoring (TDM) of colistin for over 8 years in a tertiary Spanish hospital. All variables associated with C ss,avg were selected in a multiple linear regression model that was validated in a second cohort of 40 patients. Measured C ss,avg values were compared with those predicted by our model and a previous published algorithm for critically ill patients. In total, 276 patients were enrolled [the mean age was 67.2 (13.7) years, 203 (73.6%)] were male, and the mean (SD) C ss,avg was 1.12 (0.98) mg/L. Age, gender, estimated glomerular filtration rate, CMS dose and frequency, and concomitant drugs were included in the model. In the external validation, the previous algorithm appeared to yield more optimized colistin plasma concentrations when all types of C ss,avg values (high and low) were considered, while our equation yielded a more optimized prediction in the subgroup of patients with low colistin plasma concentrations (C ss,avg <1.5 mg/L). The proposed equation may help clinicians to better use CMS among a wide variety of patients, to maximize efficacy and prevent nephrotoxicity. A further prospective PK study is warranted to externally validate this algorithm.

Prediction of the minimum ignition energy of dust clouds based on heat transfer and particle reaction kinetics

A numerical model for the calculation of the minimum ignition energy (MIE) of dust clouds is developed based on heat transfer and particle reaction kinetics. The model is an update of previous models relying on the experimental minimum ignition temperature (MIT) of dust clouds. Gas and particle temperature profiles during the spark ignition processes are studied in detail to generate dust cloud MIE prediction. For the 20 dusts studied, with the updated model, the average calculated MIE over experimental MIE ratio is 81.6%, a significant improvement compared with the ratio of 48.1% using a previous model with the MIT. A strong correlation is found between the dust cloud MIE and the MIE of the nearest particle to the spark center in the dust cloud: for all the studied dusts, the latter accounts for over 85% of the dust cloud MIE. Moreover, the minimum spark ignition temperature of a dust particle (SITP) can be defined, which also strongly correlates with the dust particle MIT (MITP) calculated using the Semenov theory. Based on these correlations, simple MIE predictive equations are derived and validated by comparing their MIE predictions with the experimental MIE data.

Viscosity of Ionic Liquids: Theories and Models.

Ionic liquids (ILs) offer a wide range of promising applications due to their unique and designable properties compared to conventional solvents. Further development and application of ILs require correlating/predicting their pressure-viscosity-temperature behavior. In this review, we firstly introduce methods for calculation of thermodynamic inputs of viscosity models. Next, we introduce theories, theoretical and semi-empirical models coupling various theories with EoSs or activity coefficient models, and empirical and phenomenological models for viscosity of pure ILs and IL-related mixtures. Our modelling description is followed immediately by model application and performance. Then, we propose simple predictive equations for viscosity of IL-related mixtures and systematically compare performances of the above-mentioned theories and models. In concluding remarks, we recommend robust predictive models for viscosity at atmospheric pressure as well as proper and consistent theories and models for P-η-T behavior. The work that still remains to be done to obtain the desired theories and models for viscosity of ILs and IL-related mixtures is also presented. The present review is structured from pure ILs to IL-related mixtures and aims to summarize and quantitatively discuss the recent advances in theoretical and empirical modelling of viscosity of ILs and IL-related mixtures.

Progressive collapse resistance prediction of truss string structures under key chord failure

Cables and lower chords at the support play a crucial role in the resistance of truss string structures (TSSs) against progressive collapse. However, the available studies have predominantly focused on cable failure, with seldom research on the progressive collapse mechanism analysis and resistance prediction under the failure of the lower chord at the support. In this study, the accuracy of the finite element (FE) model is verified based on the test results of the TSS exposed to cable failure. Secondly, the criterion for progressive collapse failure of TSSs is given, namely, the structure is considered to have suffered progressive collapse failure when the vertical displacement in the mid-span exceeds 1/60 of the structural span or local collapse occurs. Then, the mechanisms of progressive collapse failure of TSSs exposed to the failure of the lower chord at the support are outlined, and the results show that after the failure of the lower chord at the support, the load paths transmitting the support and the cable are blocked, and the support and the mid-span become the weakest locations of the damaged structure. Moreover, a simplified prediction equation for the progressive collapse resistance of TSSs exposed to the failure of the lower chord at the support is proposed in accordance with some assumptions and simplifications. The comparison reveals that there exists no >20% error between the predicted and FE values, which indicates that the equation has good accuracy and reliability. Therefore, the equation provides a certain reference value for engineering applications.

Compressive strength of beech and birch at different moisture contents and temperatures

AbstractOwing to increased environmental demands to replace petroleum-derived materials with more environmentally friendly materials, research has been directed towards the potential of using wood in the automotive industry. This study extensively investigates the compressive strength (fc) of two hardwood species (beech and birch) with different anatomical directions, at various moisture and temperature levels, in an attempt to understand the wood’s behaviour during the forming process. The experimental tests were performed at 20, 100 and 140 °C on specimens with five moisture levels, ranging from completely dry to wet conditions. Overall, irrespective of the investigated direction, the measured compressive strength exhibited a clear exponential trend with increasing moisture content over the whole temperature range. This was capitalised on to present a simple predictive equation to roughly estimate the fc of beech and birch in different moisture and temperature conditions by relating to their dry fc at a reference temperature of 20 °C. The proposed approach was compared with other procedures and trends reported in the literature for the effect of moisture and temperature on the fc of wood. Furthermore, the reduction factors for the effect of temperature on the fc of softwoods, as set out in Eurocode 5 (EN 1995-1-2), were discussed in comparison with the present study findings. It was shown that, although the Eurocode approach is conservative, it may still be applicable for estimating the fc of hardwood species.

Smooth commutation of brushless DC motor by model‐free predictive current control without back‐EMF estimation

AbstractThis study presents a new predictive control approach for brushless DC (BLDC) motor to smooth its commutation process. The proposed predictive control method is applied to the BLDC motor's equivalent DC current. Therefore, this method utilises only one prediction equation, which means less calculation than the standard predictive control schemes. Here, the equivalent DC current equation is defined according to the model‐free control theory to remove the back‐EMF voltage estimation from the control system. In consequence, the proposed predictive DC current control (DC‐PCC) has a smooth commutation. Because of applying the model‐free theory, both commutation and non‐commutation periods of the BLDC motor have similar dynamic equations. Hence, one simple prediction equation can be utilised for both periods. Besides, the proposed method has less computational burden due to using only one prediction equation and removing the back‐EMF estimation process. To validate the proposed model‐free DC‐PCC, experimental tests are performed, and the results are discussed in this research.

Numerical Modeling of Solitary Wave-Induced Flow and Scour around a Square Onshore Structure

Waves or tsunamis in the onshore area could induce severe scour at the structure foundations, threatening the stability of the structure. This paper presents a numerical study of the solitary wave-induced flow and scour around a square onshore structure. A CFD model coupled with hydrodynamic and sediment transport models is first validated through a large-scale laboratory experiment, which shows that the model can well reproduce the flow and scour characteristics. Subsequently, based on the reliable numerical results, the flow field and scour development during wave inundation of the structure are explored. It is found that the development of the simulated scour depth is faster at the early stage compared to that in the experimental result. The results also show that the scour starts at the front corner of the structure, which is also the position of the maximum scour depth. The scour develops rapidly at the early stage and is almost completed in the first half of the wave period. In addition, the results demonstrate that bed scouring increases the wave force on the structure due to the increase in the flow velocity near the bed, which needs to be considered, especially in the shallow-water scour scenario. Finally, a simplified prediction equation is proposed for the temporal development of the scour depth.

On enhancing interfacial mass transport through microextraction in dispersed droplet systems

The physio-chemical hydrodynamics and the solute transport around a translating dispersed drop are often multicomponent and multiphase systems. When such a dispersed droplet system is out of the thermodynamic equilibrium, steep momentum and concentration gradients are developed around the interface leading to rapid flow and solute transport. In broader perspectives, the dispersed droplet system has direct/indirect relevance to addressing the technological challenges in the 21st century, namely, energy storage, hydrogen production, CO2 capture, biofuel production, chemical analysis, diagnosis extraction, and drug delivery. Environmental engineering challenges include flotation and separation technology, inkjet printing, coatings, and paints. The most challenging issue in such applications is enhancing the transport rate from the droplet to the bulk of the fluid. In this work, we have proposed a novel method to foster the transport of solute from the drop to the bulk fluid by altering the viscosity of the bulk fluid. We have exploited the shear-thinning behavior of the fluid in bulk to reduce the viscous losses and resistances to the mass transfer. A linear thermodynamic equilibrium relation at the fluid-fluid interface is considered. The scant prior experimental results were observed to agree with the present findings. Therefore, the extensive numerical findings are presented for the ranges of pertinent parameters such as Reynolds number, (1≤Re≤75) Peclet number, (10≤Pe≤103), internal to external fluid diffusivity ratio, (0.5≤dratio≤4), dispersed to continuous fluid viscosity ratio, (0.25≤μratio≤4) and power-law index, (0.4≤n≤1). The drag coefficient exhibits an increase of 180% with the increase in the μratio as well as with the n at the maximum convective regime. The Sherwood number exhibits a positive dependence on the Reynolds number, Peclet number, and the diffusivity ratio. On the contrary, the viscosity ratio lowers the transport of species from the droplet to the fluid due to the reduced internal circulations inside the droplet. Shear-thinning fluid behavior offers a higher extraction efficiency than the Newtonian fluid (upto 30% increase in at low Pe). Finally, simple predictive correlative equations have been proposed for the drag coefficient and the average Sherwood number over the conditions considered here.

Effect of age and dietary crude protein level on nitrogen excretion in Holstein bull calves

The aim of the present study was to investigate the effect of age and dietary crude protein (CP) level on nitrogen (N) excretion in Holstein bull calves. Thirty-six Danish Holstein bull calves were divided into 4 age groups; 12, 22, 32, and 42 weeks old (W12, W22, W32, and W42), respectively, with 9 animals per group. Within age group, calves were blocked in groups of 3 according to age and weight, and randomly assigned to the dietary treatments in a 3 × 3 Latin square design. The dietary treatments consisted of 4 diets in total, with CP concentrations of 10.9, 14.1, 16.9, and 20.2%, of which the 3 diets with the highest CP concentrations (i.e. 14.1, 16.9, and 20.2%) were provided to W12 and W22 calves, whereas the 3 diets with the lowest CP concentrations (i.e. 10.9, 14.1, and 16.9%) were provided to W32 and W42 calves. The experiment was divided into 3 periods, all lasting for 2 weeks in which the first 11 days were used as adaptation, while the last 3 days were used for sampling and recording. Feed and water intake were recorded daily, and feces and urine samples were collected and analyzed for chemical composition. Intake of DM increased for all age groups with greater dietary CP level (P < 0.01), and was greatest in the oldest bull calves (P < 0.01). Urinary N excretion was positively correlated with N intake (P < 0.01) and age of the calves (P < 0.01). Fecal output of N increased with increasing dietary CP level (P = 0.05), but might mainly be explained by the greater feed intake observed with increasing CP level in the diet. When increasing the dietary CP level, the proportion of N intake excreted in urine increased (P < 0.01), whereas the proportion of N intake excreted in feces decreased (P < 0.01). In addition, the proportion of N intake excreted in urine was greatest in the oldest bull calves (P < 0.01). Furthermore, simple prediction equations for N excretion in urine and feces, respectively, were established and considered reliable proxies for N excretion in Holstein bull calves for application on farm.

Validation of Simple Prediction Equations for Step Count in Japanese Patients with Chronic Obstructive Pulmonary Disease.

Physical activity is decreased in patients with chronic obstructive pulmonary disease, and decreased physical activity leads to a poor prognosis. To determine an individual’s target step count from the measured step counts and predicted step counts, simple and detailed prediction equations for step count were developed. To verify the validity of the simple prediction equation, the validity of the simple equation was evaluated in a different cohort and the correlation between the step counts calculated by the simple equation and those by the detailed prediction equation were evaluated. When the step counts calculated by the simple prediction equation for all participants were compared with the measured step counts, a significant correlation was obtained among them, and the calculated values were found to be reproducible with the measured values in patients with a measured step count of <6500 by Bland–Altman plots. Furthermore, the values calculated by the simple prediction equation and those calculated by the detailed prediction equation showed a significant correlation. In conclusion, the simple prediction equation was considered reasonable.

Trends in PhysChem Properties of Newly Approved Drugs over the Last Six Years; Predicting Solubility of Drugs Approved in 2021

For the ‘small-molecule’ NMEs (new molecular entities) approved in 2021 by the US FDA, quantitative solubility values were found for 28 drugs, nearly all from published New Drug Applications (NDAs). Comparisons of physicochemical properties over the last six years indicate that the NMEs are slowly continuing to increase in size and decrease in solubility. Since 2016, the intrinsic solubility values (S0) have decreased on average by 0.50 log10 unit, the calculated octanol–water partition coefficients (clogP) have increased by 0.34 log10 unit, and the molecular weights (MW) have increased by 22 g·mol−1 (to 477, compared to 298 in older drugs). The average number of H-bond acceptors has remained constant, while the average number of H-bond donors and the Kier Φ molecular flexibility indices have decreased slightly. The reported solubility data for the 2021 small-molecule NMEs were processed using the program pDISOL-X to obtain S0 values, normalized to 25 °C. The S0 values ranged from 2 ng·mL−1 (avacopan) to 43 mg·mL−1 (viloxazine). In the new set, MW spanned from 233 g·mol−1 (dexmethylphenidate) to 1215 g·mol−1 (voclosporin). Values of clogP ranged from − 0.3 (serdexmethylphenidate, a quaternary ammonium molecule) to 8.1 (avacopan). Five different in-silico models were used to predict the aqueous intrinsic log10 solubility of the 28 novel NMEs: (i) Yalkowsky’s General Solubility Equation (GSE(classic)), (ii) Abraham’s Linear Solvation Equation (ABSOLV), (iii) Avdeef–Kansy ‘Flexible-Acceptor’ General Solubility Equation ((GSE(Φ,B)), (iv) Breiman’s Random Forest Regression (RFR), and (v) consensus model based on (ii) and (iii) above. The various models were retrained with an enlarged version of the Wiki-pS0 database (currently at 7655 log10 S0 entries of drug-relevant molecules). The consensus model (r2 = 0.67, RMSE = 1.08) just slightly outperformed the other four models. The relatively simple consensus prediction equation can be easily incorporated into spreadsheet calculations. As new drugs are approved, it will be important to continue monitoring the quality of measured solubility. Matching prediction to measurement is valuable when prediction methods are applied to virtual libraries, in order to seek opportunities to minimize pharmacokinetic risks of large, but otherwise promising, candidate molecules.

GEP tree-based computational AI approach to evaluate unconfined compression strength characteristics of Fly ash treated alkali contaminated soils

The structural failures induced by alkali-contamination of soils are well established by earlier researchers. In the present study, the effect of alkali concentration (2 M and 4 M) and duration of curing on the unconfined compressive strength (UCS) of two types of soils (Kaolin and BC soil) were evaluated. Further, the effect of fly ash (FA) addition (10%, 15%, and 20%) on the UCS of alkali-contaminated soils was also determined. The results revealed that alkali contamination resulted in reducing UCS (by 36% and 46%), observed for kaolin and BC soil, respectively. The addition of FA has resulted in a linear increase in UCS of both soils. The optimum content was noted at 15%, attributed to the pozzolanic reaction initiated by alkali and subsequent gain of UCS with the formation of calcium (with FA addition) based hydration compounds. In addition, three attributes, i.e., FA content, curing period, and alkali concentration, have been used to develop GEP tree-based models to furnish simple prediction equations for calculating the UCS of Kaolin and BC soils. The accuracy of the developed models were initially validated using various statistical checks such as R, MAE, RMSE and RSE. For the 2nd level validation, parametric and sensitivity analyses were carried out for both soils, which yielded a comparable variation and contribution of each input conforming to the literature.

Effect of age and dietary crude protein level on nitrogen excretion in dairy heifers

The aim of this study was to examine the effect of age and dietary crude protein (CP) level on nitrogen (N) excretion in dairy heifers and to develop simple models to predict N excretion on farm based on nutrient intake parameters. Thirty-six Danish Holstein heifers were divided into 4 age groups; 8, 12, 16, and 20 months old (M8, M12, M16, and M20), respectively, with 9 animals per group. Heifers within age groups were blocked in groups of 3 according to age and pregnancy stage and were randomly assigned to dietary treatments in a 3 × 3 Latin square design. The dietary treatments consisted of 4 diets in total with CP concentrations of 9.8, 12.2, 14.6, and 17.0%, respectively, of which the 3 diets with the highest CP concentrations (i.e. 12.2, 14.6, and 17.0%) were provided to M8 and M12 heifers, and the 3 diets with the lowest CP concentrations (i.e. 9.8, 12.2, and 14.6%) were provided to M16 and M20 heifers. The experiment was divided into 3 periods, each period lasting for 2 weeks of which the first 11 days were used as adaptation and the last 3 days were used for sampling and recording. Feed and water intake were recorded daily, and feces (n = 6, pooled) and urine samples (n = 2, pooled) were collected and analyzed for chemical composition. Fecal output of N increased with increased dietary CP level, and this was probably due to the increase in dry matter intake (DMI) and N intake observed when increasing dietary CP level. Urinary N excretion increased with increased CP level in the diet, and was greatest in older heifers. Thus, for dairy heifers, urinary N output appeared to be strongly correlated with dietary N intake and seemed to increase with age. A dietary CP level of 12.2% seemed sufficient for heifers in all age groups to maintain adequate rumen microbial digestion, underpinning the potential of reducing urinary N by dietary manipulation. However, due to the short experimental periods, this study cannot state whether this CP level fulfills the N requirement for growth during the whole rearing period of dairy heifers. Furthermore, a simple prediction equation was established for N excretion in feces: N feces (g/d) = 0.054 × N intake + 6.52 × DMI and was considered a reliable proxy for N excretion in replacement heifers for application on farm.

Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches

Foamed concrete is special not only in terms of its unique properties, but also in terms of its challenging compositional mixture design, which necessitates multiple experimental trials before obtaining the desired property like compressive strength. Regardless of design challenges, artificial intelligence (AI) techniques have shown to be useful in reliably estimating desired concrete properties based on optimized mixture proportions. This study proposes AI-based models to predict the compressive strength of foamed concrete. Three novel AI approaches, namely artificial neural network (ANN), gene expression programming (GEP), and gradient boosting tree (GBT) models, were employed. The models were developed using 232 experimental results, considering easily acquired variables, such as the density of concrete, water-cement ratio and sand-cement ratio as inputs to estimate the compressive strength of foamed concrete. In training the models, 80% of the experimental data was used and the rest was used to validate the models. The optimized models were selected using their respective best hyper-parameters on trial and error basis; variable number of hidden layers, number of neurons and training algorithms were used for ANN, number of chromosomes, head size, number of genes, variable function set for the GEP and GBT employed number of trees, maximal depth and learning rate. The trained models were validated using parametric and sensitivity analyses of a simulated dataset. The prediction abilities of proposed models were evaluated using the coefficient of correlation (R), mean absolute error (MAE), and root mean squared error (RMSE). For the validation data, empirical results from the performance evaluation revealed that GBT model (R = 0.977, MAE = 1.817 and RMSE = 2.69) has relative superior performance with highest correlation and least error in comparison with ANN (R = 0.975, MAE = 2.695 and RMSE = 3.40) and GEP (R = 0.96, MAE = 2.07 and RMSE = 2.80). The study concludes that the developed GBT model offered reliable accuracy in predicting the compressive strength of foamed concrete. Finally, the simple prediction equation generated from the GEP model signifies its importance and can reliably be used in estimating compressive strength of foamed concrete. It is recommended that the prediction models shall be used for the ranges of input variables employed in this study.

Multi Expression Programming Model for Strength Prediction of Fly-Ash-Treated Alkali-Contaminated Soils.

Rapid industrialization is leading to the pollution of underground natural soil by alkali concentration which may cause problems for the existing expansive soil in the form of producing expanding lattices. This research investigates the effect of stabilizing alkali-contaminated soil by using fly ash. The influence of alkali concentration (2 N and 4 N) and curing period (up to 28 days) on the unconfined compressive strength (UCS) of fly ash (FA)-treated (10%, 15%, and 20%) alkali-contaminated kaolin and black cotton (BC) soils was investigated. The effect of incorporating different dosages of FA (10%, 15%, and 20%) on the UCSkaolin and UCSBC soils was also studied. Sufficient laboratory test data comprising 384 data points were collected, and multi expression programming (MEP) was used to create tree-based models for yielding simple prediction equations to compute the UCSkaolin and UCSBC soils. The experimental results reflected that alkali contamination resulted in reduced UCS (36% and 46%, respectively) for the kaolin and BC soil, whereas the addition of FA resulted in a linear rise in the UCS. The optimal dosage was found to be 20%, and the increase in UCS may be attributed to the alkali-induced pozzolanic reaction and subsequent gain of the UCS due to the formation of calcium-based hydration compounds (with FA addition). Furthermore, the developed models showed reliable performance in the training and validation stages in terms of regression slopes, R, MAE, RMSE, and RSE indices. Models were also validated using parametric and sensitivity analysis which yielded comparable variation while the contribution of each input was consistent with the available literature.

Energy expenditure in COVID-19 mechanically ventilated patients: A comparison of three methods of energy estimation.

BackgroundIndirect 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.MethodsRetrospective 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.ResultsIn 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.ConclusionThe 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.

Crown area predicts total biomass for Rhodomyrtus tomentosa, an invasive shrub in Florida

AbstractPredictive models of aboveground plant biomass derived from nondestructive measurements greatly assist in monitoring and surveying natural areas. Where invasive species are concerned, these models can provide insights to the impacts of invasions and efficacy of management strategies. Furthermore, tools that facilitate a rapid inventory allow for multiple assessments of impact over larger areas. Downy rose myrtle [Rhodomyrtus tomentosa (Aiton) Hassk.] is an invasive shrub in Florida and Hawaii that is native to southeastern Asia. Rhodomyrtus tomentosa was imported into Florida in the early 20th century through the ornamental plant trade and produces pink flowers and edible purple globe fruits. This woody shrub is particularly problematic in the understory of Florida’s mesic pine forests, where it forms dense, impenetrable thickets. To characterize the populations more accurately in Florida and build predictive equations for biomass that could be used to inform control methods, we established a network of sites from which we harvested individuals over 3 yr. Based on these measurements, we built a simple predictive equation for R. tomentosa dry biomass. Crown area strongly associates with biomass in a linear relationship (P &lt; 0.001, R2 = 0.82). Fruit production is highly variable, but positively correlates to plant height in individuals that have reached reproductive size (plants below 1 m generally do not produce fruit), albeit weakly (P &lt; 0.002, R2 = 0.27). We demonstrate here that two simple measurements—height and crown area—can accurately predict biomass and, to some degree, fruit production for R. tomentosa in Florida and may guide control methods by focusing on removing individuals larger than 1 m tall.

Development of an easy-to-use prediction equation for waist circumference based on BMI and body weight among a sample of Egyptian women

Background/aim Although both BMI and waist circumference (WC) estimate the level of fatness, WC may be more crucial because of its deep relationship to cardiovascular diseases. As WC is not ascertained regularly in clinical practice, this study intended to develop and substantiate an easy to use WC prognostic equation based on either BMI or body weight, appropriate for Egyptian women. Patients and methods A cross-sectional study included 505 Egyptian women, aged 20–60 years. Anthropometric measurements (body weight, height, and WC) were evaluated and BMI was estimated. A total of 329 women were used for construction of simplified prediction equations (learning sample) and another 176 women for assessing their legality (validation sample). Pearson's correlation coefficient, the calibration slope, and R 2 for the regression of the measured WC versus the predicted WC were used to assess execution of the equations in the validation sample. Results There were insignificant differences between the two samples in the measurements and BMI classification. The preponderance of the two samples were suffering from obesity (84.5 vs. 79.5%) and overweight (11.9 vs. 17.0%), respectively. The following simple equations were obtained to predict WC of Egyptian women: WC=48.44+(1.471×BMI) or WC=57.53+(0.487×weight). There were insignificant differences in means±SD of the measured versus predicted WC among both samples. Conclusions These equations precisely estimate WC. It should be helpful for medical care practitioners and public health personnel who like to detect persons and populations at risk for cardiovascular disease when the WC data are unobtainable.

Simulation of Slope Failure Distributions Due to Heavy Rain on an Island Composed of Highly Weathered Granodiorite Based on the Simple Seepage Analysis

To fully and rapidly develop a real-time early warning judgment system for slope failure at the time of heavy rains including overseas, it is necessary to predict water movement in the soil at the time of rainfall. In addition, to apply the system to a place where insufficient geotechnical and geological data have been amassed, it is necessary to evaluate the risk of slope failure based on physical properties obtained from a simple soil test. Therefore, in this study, the authors set Gogoshima Island in Ehime Prefecture as a study site and evaluated the water movement over time in the soil during heavy rain using a simple prediction equation of rainfall seepage process. Soil properties were determined through simple in-situ and laboratory tests. As a result, it was found that the factor of safety for slope failure in the head and wall of a valley dissecting the hillside slope composed of granodiorite in which weathering has progressed can be planarly evaluated using the simple prediction equation.