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This paper introduces a novel logarithmic beta generated family of distributions, designed specifically for the nuanced analysis of medical data. Through meticulous development, we have establish a comprehensive suite of distributional properties, including quantiles, moments, and hazard rate functions. Central to our contributions is the exploration of the power function distribution, a pivotal sub-model within this innovative family, demonstrating unparalleled flexibility across a broad spectrum of data types. Our investigation delves into seven prominent parameter estimation techniques, rigorously assessing their efficacy through both simulation studies and real-life medical datasets. This comparative analysis highlights the superior performance of the proposed model against existing standards, particularly through improved goodness-of-fit measures across four distinct medical science lifetime datasets. The findings highlight the new model's dominance in capturing complex data patterns, attributable to its adaptable density and hazard rate functions. These attributes significantly enhance modeling capabilities, paving the way for more precise and reliable medical research outcomes. The study concludes by emphasizing the model's potential in advancing statistical methodologies for medical science, backed by theoretical and empirical validations of its superior performance and versatility. [ABSTRACT FROM AUTHOR]

This paper introduces a novel logarithmic beta generated family of distributions, designed specifically for the nuanced analysis of medical data. Through meticulous development, we have establish a comprehensive suite of distributional properties, including quantiles, moments, and hazard rate functions. Central to our contributions is the exploration of the power function distribution, a pivotal sub-model within this innovative family, demonstrating unparalleled flexibility across a broad spectrum of data types. Our investigation delves into seven prominent parameter estimation techniques, rigorously assessing their efficacy through both simulation studies and real-life medical datasets. This comparative analysis highlights the superior performance of the proposed model against existing standards, particularly through improved goodness-of-fit measures across four distinct medical science lifetime datasets. The findings highlight the new model's dominance in capturing complex data patterns, attributable to its adaptable density and hazard rate functions. These attributes significantly enhance modeling capabilities, paving the way for more precise and reliable medical research outcomes. The study concludes by emphasizing the model's potential in advancing statistical methodologies for medical science, backed by theoretical and empirical validations of its superior performance and versatility.

Abstract Chemical graph theory, a subfield of graph theory, is used to investigate chemical substances and their characteristics. Chemical graph analysis sheds light on the connection, symmetry, and reactivity of molecules. It supports chemical property prediction, research of molecular reactions, drug development, and understanding of molecular networks. A crucial part of computational chemistry is chemical graph theory, which helps researchers analyze and manipulate chemical structures using graph algorithms and mathematical models. Beryllonitrene , a compound of interest due to its potential applications in various fields, is examined through the lens of graph theory and mathematical modeling. The study involves the calculation and interpretation of topological indices and graph entropy measures, which provide valuable insights into the structural and energetic properties of Beryllonitrene’s molecular graph. Logarithmic regression models are employed to establish correlations between these indices, entropy, and other relevant molecular attributes. The results contribute to a deeper understanding of Beryllonitrene’s complex characteristics, facilitating its potential applications in diverse scientific and technological domains. In this study, degree-based topological indices $$\text{TI}$$ TI are determined, as well as the entropy of graphs based on these $$\text{TI}$$ TI .

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Chemical graph theory, a subfield of graph theory, is used to investigate chemical substances and their characteristics. Chemical graph analysis sheds light on the connection, symmetry, and reactivity of molecules. It supports chemical property prediction, research of molecular reactions, drug development, and understanding of molecular networks. A crucial part of computational chemistry is chemical graph theory, which helps researchers analyze and manipulate chemical structures using graph algorithms and mathematical models. Beryllonitrene , a compound of interest due to its potential applications in various fields, is examined through the lens of graph theory and mathematical modeling. The study involves the calculation and interpretation of topological indices and graph entropy measures, which provide valuable insights into the structural and energetic properties of Beryllonitrene's molecular graph. Logarithmic regression models are employed to establish correlations between these indices, entropy, and other relevant molecular attributes. The results contribute to a deeper understanding of Beryllonitrene's complex characteristics, facilitating its potential applications in diverse scientific and technological domains. In this study, degree-based topological indices TI are determined, as well as the entropy of graphs based on these TI . [ABSTRACT FROM AUTHOR]

This research aims to examine the influence of human skull bone thickness and density on light penetration in PBM therapy across different wavelengths, focusing on how these bone characteristics affect the absorption of therapeutic light. Analyses explored the effect of skull bone density and thickness on light penetration in PBM, specifically using Low-Level Laser Therapy (LLLT) for efficacy prediction. Measurements of bone thickness and density were taken using precise tools. This approach emphasizes LLLT's significance in enhancing PBM outcomes by assessing how bone characteristics influence light penetration. The study revealed no significant correlation between skull bone density and thickness and light penetration capability in photobiomodulation (PBM) therapy, challenging initial expectations. Wavelengths of 405 nm and 665 nm showed stronger correlations with bone density, suggesting a significant yet weak impact. Conversely, wavelengths of 532 nm, 785 nm, 810 nm, 830 nm, 980 nm, and 1064 nm showed low correlations, indicating minimal impact from bone density variations. However, data variability (R2 < 0.4) suggests that neither density nor thickness robustly predicts light power traversing the bone, indicating penetration capability might be more influenced by bone thickness at certain wavelengths. The study finds that the effectiveness of photobiomodulation (PBM) therapy with bone isn't just based on bone density and thickness but involves a complex interplay of factors. These include the bone's chemical and mineral composition, light's wavelength and energy dose, treatment duration and frequency, and the precise location where light is applied on the skull. [ABSTRACT FROM AUTHOR]

Objective: In order to predict and control the hot air drying process of Mango. Methods: Using fresh Mangifera indica Linn as experimental material, the effects were studied in different hot air temperatures (60, 65, 70 ℃) and different thickness of Mango slices (0.8, 1.0, 1.2 cm) on the drying curve, drying characteristic curve and effective moisture diffusion coefficient of Mango. Selecting six commonly used drying models suitable for fruits and vegetables for fitting, analysis, and validation, the most suitable model for Mango hot air drying was selected. Results: With the increase of temperature and the decrease of slice thickness, the drying rate of Mango slices was accelerated, resulting in shorter drying times. The effective diffusion coefficient of water increases with the increase of temperature and thickness, in the range of 1.401 39×10-10 to 3.655 46×10-10 m2/s. R2 of Logarithmic model was the largest, and X2 and RMSE were the smallest, which were 0.998 87, 0.000 124 779 and 0.001 37 respectively. Conclusion: The predicted values are basically consistent with the experimental values, accord with Mango hot air drying, it can better reflect the changing law of moisture content of Mango slices during drying.

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Choosing the most appropriate mathematical model for establishing a simple regression between two variables which represent two economic indicators or from other scientific fields is an approach that involves many and often quite difficult actions. In the article we rigorously presented the stages to be followed in order to obtain the defining parameters for nine models: linear, power (double logarithmic), exponential, logarithmic, three hyperbolic models (the third is also a Tӧrnqvist-type model) and two exponential-hyperbolic models. The final stage in establishing the most appropriate model is less discussed in the paper. Thus, the simple regression for which the model has the highest Pearson correlation coefficient is chosen as optimal in the article.

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ability to obtain an accurate measure of irrigation water use is urgently needed in order to provide further scientific guidance for irrigation practices. This investigation took soil moisture and precipitation as the study objects and quantitatively analyzed their relationship by establishing four models: a linear model, a logarithmic model, a soil water balance model, and a similarity model. The results from building models on every site clearly revealed the relationship between soil moisture and precipitation and confirmed the feasibility of estimating irrigation water use when soil moisture data are known. Four models combined with soil moisture data were used to estimate irrigation water use. First, the 16 sites which monitor soil moisture conditions in Hebi City were identified as study objects, from which everyday meteorological data (temperature, precipitation, atmospheric pressure, wind speed, sunshine duration) and soil moisture data from 2015 to 2020 (totaling six years) were collected. Second, the eligible data from the first four years in the date range were used to create four kinds of models (linear model, logarithmic model, soil water balance model, and similarity model) to estimate the amount of water input to the soil surface based on soil moisture. Third, the eligible data from the last two years in the established date range were used to verify the established models on every site and then judge the accuracy of the models. For example, for site 53990, the RMSE of the linear model, logarithmic model, soil water balance model, and similarity model was 10,547, 10,302, 8619, and 7524, respectively. The results demonstrate that the similarity model proposed in this study can express the quantitative relationship between soil moisture and precipitation more accurately than the other three models. Based on this conclusion, the eligible soil moisture data known in the specific site were ultimately used to estimate the irrigation water use in the field by the relationship expressed in the similarity model. Compared with the amount of irrigation water data recorded, the estimated irrigation water use yielded by the similarity model in this study was 18.11% smaller. In a future study, microwave satellite remote sensing of soil moisture data, such as SMAP and SMOS soil moisture data, will be used to evaluate the performance of estimated regional irrigation water use. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Compressive strength is the most important mechanical index of ultra-high performance fiber-reinforced concrete (UHPFRC). The rule of changes in compressive strength in early-aged UHPFRC is of great significance to guide concrete curing, formwork removal, and prestress stretching. Therefore, it is very necessary to study an accurate mathematical model to describe the change in compressive strength of UHPFRC at an early age. For this purpose, a new mathematical model of compressive strength age is proposed in this work for predicting the long-term strength of UHPFRC according to a few test data from early-aged UHPFRC. This new model can overcome the shortcomings of the existing models, such as the exponential model, logarithmic model, and polynomial model. The proposed model is first demonstrated by using four groups of compressive strength test data compiled from previous research studies. Subsequently, an experiment of early-aged UHPFRC compressive strength was carried out to further verify the proposed mathematical model. The mixed proportion used in the UHPFRC compressive strength test was 10.87:0.82:1 (powder:steel fiber:water), and the design strength grade was 120 MPa. Based on the UHPFRC experimental data, it was shown that the average fitting error and standard deviation of the new model were about 10%~20% of that of the logarithmic model and the polynomial model. The proposed model can precisely predict the compressive strength of UHPFRC, with a determination coefficient (R2) of 0.9974. The research results show that the average fitting error and standard deviation of this new model were significantly reduced when compared to the existing models, and the predicted compressive strength by the new model on the 60th day is the closest to the actual design strength grade of concrete. The greatest advantage of the proposed method lies in its simple formula, fast implementation, and no need for complex mathematical operations. It has been shown that the proposed model is superior to the existing models due to its higher fitting accuracy and prediction accuracy, and it can be better used to predict the later strength of UHPFRC by using only a few compressive strength test data taken at the early age stage. [ABSTRACT FROM AUTHOR]

In this paper, a generalized population model with delays and harvesting term is studied, which includes some well-known models, such as Gilpin–Ayala competitive model and Logarithmic model. By a suitable Lyapunov function and the Banach fixed point theorem, we obtain the existence and uniqueness of globally attractive pseudo almost periodic solution of the model and prove its permanence. Some examples and numerical simulations are given to illustrate the feasibility and usefulness of the model and results. [ABSTRACT FROM AUTHOR]

This study was conducted to obtain semi-empirical mathematical models that represent the convective drying kinetics of squash epicarp at different temperatures (55, 65 and 75°C/10% RH/ drying speed 1ms-1). The best drying condition was selected to obtain flour, which was then characterized and used for the preparation of glutenfree bread. Subsequently, the effect of incorporating squash epicarp flour (SEF) into 4 levels (0-control, 15, 20 and 25g/100 g of flour) on the sensory acceptance of the bread was evaluated. The results of this study are that the two-term model best describes the curve obtained at 55 °C, and the logarithmic model best describes the curves obtained at 65 °C and 75 °C. The temperature of 65 °C was selected as the best drying condition. Finally, the highest acceptance and purchase intention was obtained from the bread produced with 15 g SEF/100 g of flour. [ABSTRACT FROM AUTHOR]

The paper presents the results of statistical processing of data on the thickness and size of the tephra fragments of andesidacite composition erupted as a result of a phreatic explosion in the caldera of Golovnin volcano about 1000 years ago. A petrochemical description of the products of volcanic activity of the crustal Golovnin volcano and its evolution process is presented based on geological and geophysical data. The relationship between the thickness of the tephra, the size of its fragments, and the distance to the eruption center was studied using the polynomial regressions of varying degrees and exponential distribution. The adequacy of the constructed models to the initial data is illustrated by determination coefficients. Tephra distribution models were constructed on the basis of a three-dimensional trend analysis. For the first time, a logarithmic model was used to describe the size of tephra fragments. The accuracy of the model used was estimated. A method for estimating the potential dispersion range of tephra fragments of a certain size was obtained. The work demonstrates the potential of mathematical statistics for describing the distribution of products of volcanic eruptions of a certain type. The results of this study are suitable for creating an information database of pyroclastite distribution across the Kuril-Kamchatka volcanic region.

The ability to obtain an accurate measure of irrigation water use is urgently needed in order to provide further scientific guidance for irrigation practices. This investigation took soil moisture and precipitation as the study objects and quantitatively analyzed their relationship by establishing four models: a linear model, a logarithmic model, a soil water balance model, and a similarity model. The results from building models on every site clearly revealed the relationship between soil moisture and precipitation and confirmed the feasibility of estimating irrigation water use when soil moisture data are known. Four models combined with soil moisture data were used to estimate irrigation water use. First, the 16 sites which monitor soil moisture conditions in Hebi City were identified as study objects, from which everyday meteorological data (temperature, precipitation, atmospheric pressure, wind speed, sunshine duration) and soil moisture data from 2015 to 2020 (totaling six years) were collected. Second, the eligible data from the first four years in the date range were used to create four kinds of models (linear model, logarithmic model, soil water balance model, and similarity model) to estimate the amount of water input to the soil surface based on soil moisture. Third, the eligible data from the last two years in the established date range were used to verify the established models on every site and then judge the accuracy of the models. For example, for site 53990, the RMSE of the linear model, logarithmic model, soil water balance model, and similarity model was 10,547, 10,302, 8619, and 7524, respectively. The results demonstrate that the similarity model proposed in this study can express the quantitative relationship between soil moisture and precipitation more accurately than the other three models. Based on this conclusion, the eligible soil moisture data known in the specific site were ultimately used to estimate the irrigation water use in the field by the relationship expressed in the similarity model. Compared with the amount of irrigation water data recorded, the estimated irrigation water use yielded by the similarity model in this study was 18.11% smaller. In a future study, microwave satellite remote sensing of soil moisture data, such as SMAP and SMOS soil moisture data, will be used to evaluate the performance of estimated regional irrigation water use.

Con este estudio se obtuvieron modelos matemáticos semi-empíricos que representan la cinética de secado convectivo del epicarpio de zapallo a diferentes temperaturas (55, 65 y 75 ° C / 10% HR / velocidad de secado 1ms-1). Se seleccionó la mejor condición de secado con la que se obtuvo la harina, la cual se caracterizó y utilizó para la elaboración de la torta libre gluten. Posteriormente, se evaluó el efecto de incorporar harina de epicarpio de calabaza en 4 niveles (control 0, 15, 20 y 25g / 100 g de harina) sobre la aceptación sensorial de la torta. Como resultados se obtuvo que el modelo de dos términos describe mejor la curva obtenida a 55 ° C, y el modelo logarítmico describe mejor las curvas obtenidas a 65 ° C y 75 ° C. Se seleccionó la temperatura de 65 ° C como la mejor condición de secado. Finalmente, la mayor aceptación e intención de compra se obtuvo en el pan formulado con 15 g / 100 g de harina de epicarpio de calabaza.

Choosing the most appropriate mathematical model for establishing a simple regression between two variables which represent two economic indicators or from other scientific fields is an approach that involves many and often quite difficult actions. In the article we rigorously presented the stages to be followed in order to obtain the defining parameters for nine models: linear, power (double logarithmic), exponential, logarithmic, three hyperbolic models (the third is also a T?rnqvist-type model) and two exponential-hyperbolic models. The final stage in establishing the most appropriate model is less discussed in the paper. Thus, the simple regression for which the model has the highest Pearson correlation coefficient is chosen as optimal in the article. [ABSTRACT FROM AUTHOR]

We introduce time-inhomogeneous stochastic volatility models, in which the volatility is described by a nonnegative function of a Volterra type continuous Gaussian process that may have very rough sample paths. The main results obtained in the paper are sample path and small-noise large deviation principles for the log-price process in a time-inhomogeneous super rough Gaussian model under very mild restrictions. We use these results to study the asymptotic behavior of binary barrier options, exit time probability functions, and call options. [ABSTRACT FROM AUTHOR]

During the last two decades, technological development has led to an extreme increase in transport and connected industries. This has significantly increased the production of automobile tires, which have their life span after which they go to waste. It is therefore necessary to extricate new products from recycled raw materials. For a product to be created, it is necessary, especially in civil engineering, to examine and determine all the properties of this material (pressure, traction, stress resistance, etc.). The results obtained by the research in this paper are mathematically processed by applying a logarithmic model. The aim of this process is to predict stress deformation in terms of tensile force. The model obtained is significant with accuracy of 87.21% and has a very high accuracy of the deformation estimation in relation to the applied stress. Independent variables were granulometric composition, binder (glue) and specific mass.

During the last two decades, technological development has led to an extreme increase in transport and connected industries. This has significantly increased the production of automobile tires, which have their life span after which they go to waste. It is therefore necessary to extricate new products from recycled raw materials. For a product to be created, it is necessary, especially in civil engineering, to examine and determine all the properties of this material (pressure, traction, stress resistance, etc.). The results obtained by the research in this paper are mathematically processed by applying a logarithmic model. The aim of this process is to predict stress deformation in terms of tensile force. The model obtained is significant with accuracy of 87.21% and has a very high accuracy of the deformation estimation in relation to the applied stress. Independent variables were granulometric composition, binder (glue) and specific mass. [ABSTRACT FROM AUTHOR]

The HP TiO2 model, as it is well known, is the most widely used physical model of memristor. However, deriving a mathematical model that fully characterizes the HP TiO2 memristor is a challenging task. As a result, simplified models such as the nonlinear quadratic model and the cubic memristor model are utilized in theoretic quantitative analysis of memristor circuits. These models result in unsatisfactory performance for many applications. To mitigate this problem, this paper proposes a new nonlinear logarithmic model to characterize memristor. Additionally, a memristor emulator circuit is developed. Finally, the relationships among the HP TiO2 memristor, the logarithmic model, and the emulator are thoroughly discussed. [ABSTRACT FROM AUTHOR]

This work presents the drying process of apricots and apples which have been considered for drying in a solarassisted forced convection tray dryer. The logarithmic model has been used to describe the drying behavior of apples and apricots at different air temperatures (50 °C to 80 °C) and at different air velocities (0.5 m/s to 2 m/s) based on experimental data from several studies. The slice thickness of apples has been assumed at 10 mm with initial moisture content 87 % d. b; the main diameter of apricots was 40 mm with initial moisture content 80 % d. b. The changes in moisture content with drying time during the drying period have been presented indicating the absence of the constant rate period. In addition, drying air flow rates and temperatures had an important effect on the drying time and on the moisture removal from apricots and apples. Finally, the effective moisture diffusivity values have been estimated from Fick's diffusion model pointing out that it has been increased with the increase of the drying air temperature. [ABSTRACT FROM AUTHOR]

Chopped green bell pepper pieces were blanched (95 °C, 5 min) and chemically pretreated (1% potassium metabisulphite solution, 25 min at room temperature) before drying in hot air dryer (HAD) at various temperature ranges (60 – 80 °C). Three vacuum levels (200, 400, 600 mm Hg) and microwave power levels (100, 200, 300 W) were also used to dry green bell pepper samples in a vacuum assisted microwave (VAM) (2.45 GHz, 0.8 kW) dryer. VAM drying methods offered a maximum reduction by four to five times in drying time as compared to that in HAD. The logarithmic model was found to have the best fit based on high R2 and small values of reduced χ2 and RMSE. VAM method has higher values for effective moisture diffusivity (Deff) and lower values for activation energy (Ea), in comparison to the HAD method.

Chopped green bell pepper pieces were blanched (95 °C, 5 min) and chemically pretreated (1% potassium metabisulphite solution, 25 min at room temperature) before drying in hot air dryer (HAD) at various temperature ranges (60 - 80 °C). Three vacuum levels (200, 400, 600 mm Hg) and microwave power levels (100, 200, 300 W) were also used to dry green bell pepper samples in a vacuum-assisted microwave (VAM) (2.45 GHz, 0.8 kW) dryer. VAM drying methods offered a maximum reduction by four to five times in drying time as compared to that in HAD. The logarithmic model was found to have the best fit based on high R² and small values of reduced X² and RMSE. VAM method has higher values for effective moisture diffusivity (Deff) and lower values for activation energy (Ea), in comparison to the HAD method. [ABSTRACT FROM AUTHOR]

The commitment toward energy efficiency has been taken seriously in several manufacturing sectors, specifically in the building industry. By the end of the seventies and during the early eighties, the research tackled the topic of the energy embodied in commodities and goods, the construction materials as well. In the last few years, embodied energy (EE) has gone back to be a prominent research field, due to the growing awareness that the energy initially used to produce goods and services might prevail in determining the whole amount of life-cycle energy. This is not at all surprising considering high-performance buildings as the passive houses. Here we show that the EE level of several materials is already summarized by well-known and widely available parameters, namely their production costs or market prices. The ability to explain the EE level, through market data arising from production processes, sharply increases by dividing the building materials into clusters, according to their reference industry. The results show a logarithmic relation between EE and cost. Once the EE exceeds a certain threshold the cost increases more than proportionally. Therefore, the will to make rational consumption and production decisions entails the need to consider the energy-to-cost ratio. [ABSTRACT FROM AUTHOR]

This paper presents the results of a laboratory experiment of swash hydrodynamics on a coarse sand barrier beach backed by a lagoon. Boundary layer dynamics have been analyzed using the high-resolution near-bed velocities measured by Acoustic Doppler Velocity Profilers deployed in the swash zone. Swash events have been ensemble-averaged in order to study mean hydrodynamic patterns. A proposed velocity gradient criterion allowed identification of the boundary layer growth during the backwash phase, but it was unable to characterize boundary layer variability during uprush. Cross-shore velocity profiles were well represented by the logarithmic model for a large portion of the ensemble-averaged swash duration. Uprush and backwash logarithmic-estimated friction factors were of the same order of magnitude with a strong variability related to the boundary layer growth during the backwash. The momentum integral method provided smaller bed shear stresses than the logarithmic model, a result possibly related to either the assumptions involved in the momentum integral method or to an underestimation of the boundary layer thickness during uprush. A decrease of friction coefficients for increasing Reynolds numbers at the early backwash was observed. This behavior is consistent with traditional results for steady and uniform flows in a transitional regime. [ABSTRACT FROM AUTHOR]

Medidas precisas de volume de madeira são ferramentas importantes no planejamento do uso do recurso florestal. Neste estudo, foram investigados modelos volumétricos para a Floresta Nacional do Tapirapé-Aquirí, na Serra dos Carajás (PA), baseados numa cubagem rigorosa de 55 árvores para obter o diâmetro, altura comercial do fuste e volume sólido. Um total de 8 modelos de dupla entrada e 4 de simples entrada foram testados para o diâmetro mínimo de 14 cm. Para seleção do melhor modelo foram usadas as estatísticas do coeficiente de determinação, erro padrão da estimativa e distribuição dos resíduos. Entre os modelos de simples entrada o modelo logarítmico de Husch se ajustou melhor aos dados (R² = 0,9105) e entre os de dupla entrada o logarítmico de Schumacher & Hall se ajustou melhor (R² = 0,9942). O uso do modelo da Flona de Tapajós ou o uso do modelo de volume cilíndrico com fator de forma 0,7 subestimam a volumetria na Flona do Tapirapé. Isso enfatiza a importância de modelos volumétricos locais para melhorar a precisão da estimativa de madeira.Accurate volume estimate is a crucial variable for effective forest management in the tropics. In this study, we investigated volumetric models for Tapirapé-Aquirí National Forest in Carajás, PA, Brazil, based on 55 cubed trees by obtaining dbh, stem commercial height, and solid stem volume. A total of 8 double- and 4 single-entry models were tested for minimum dbh at 14 cm. Statistical procedures, such as coefficient of determination (R²), distribution of residuals, and standard error of estimate, were used to evaluate the accuracy of the estimates. Among simple-entry models, Husch logarithmic model showed the best fit to the data (R² = 0.9105), whereas Schumacher & Hall logarithmic model showed the best performance among double-entry models (R² = 0.9942). One previously published regression model, developed for Tapajós National Forest, and a taper function with a form factor of 0.70 were also cross-validated against the trees of this study. These two models subestimated the true volume. This study therefore highlights the importance of developing local volumetric models in order to obtain accurate estimates.

The objective of this study was to establish the feasibility of applying the Page, Two term exponential, Newton, Logarithmic and Henderson and Pabis mathematical drying models for predicting the drying kinetics of seed maize using super absorbent hydrogel under hermetic conditions. To achieve this, the study was conducted using hydrogel to seed maize ratios by weight of 0:1, 1:5, 1:10 and 1:15 at four different drying temperature levels of 25oC, 30oC, 35oC and 40oC at different initial seed maize moisture contents of 16%, 28% and 53% (dry basis). The moisture data obtained were fitted into the five drying models using non-linear regression analysis (MS Excel 2003TM) based on the minimization of sum of squares by adjusting the model constants. The coefficient of determination (R2), root mean square error (RMSE) and chi-square (X2) were criteria for selecting the best model. High values of coefficient of determination (R2 > 0.95) were obtained for all the five drying models while the corresponding values of X2 and RMSE were in the range of (0.0016-0.0141) and (0.0400-0.3045) respectively. As a result Logarithmic model was the best fitted model with R2 (0.9749-0.9876) and the weakest values of X2 (0.0016-0.0036) and RMSE (0.04-0.128). [ABSTRACT FROM AUTHOR]

Cross-shore velocity profiles are measured at 0.001 m vertical resolution and at 100 Hz over the lower 0.02–0.07 m of the water column in the mid swash zone on a dissipative, macrotidal beach. Swash motion is predominantly at infragravity frequencies and forced by significant wave heights exceeding 1.5 m and peak wave periods over 15 s. Observations of long duration (> 14 s) swashes during two rising tides are used to quantify the vertical structure of cross-shore flow velocities and estimate corresponding bed shear stress and friction coefficients. Analysis is performed on an individual swash event to an elevation of 0.07 m and an ensemble event made up of 24 individual swash events to an elevation of 0.02 m. Cross-shore velocities exceed 2 m s −1 and are of a similar magnitude during both the uprush and the backwash. Changes in velocity with elevation indicate that the swash zone boundary layer extends to 0.07 m during the strongest flows and is well-represented by the logarithmic model applied to this elevation, except near flow reversal. Maximum bed shear stresses estimated using the logarithmic model are 22 N m −2 and 10 N m −2 for the individual event and ensemble event respectively and mean values are larger during the backwash than the uprush. Mean friction coefficients estimated from equating the logarithmic model and the quadratic drag law are 0.018 and 0.019 for the individual event and ensemble event respectively. Bed shear stress may be underestimated if the logarithmic model is fit to a velocity profile that is only part boundary layer, emphasising the need for high resolution velocity profiles close to the bed for accurate bed shear stress predictions in the swash zone. [ABSTRACT FROM AUTHOR]

The dimorphism in reproductive allometry has received much attention, while rather little is known about the sexual dimorphism in the vegetative allometry of a dioecious species. This study presents vegetative and reproductive allometries for two dioecious tree species of the genus Rhamnus in north-eastern China. A power function and a linear model were found suitable for describing different components of the vegetative allometry. In addition, a linear regression was used to estimate the number of flowers and/or fruits based on observed stem size. R. davurica being the taller species of the two shows highly significant correlations for all selected combinations of different vegetative components. These correlations are much less pronounced and sometimes even nonsignificant in R. schneideri, which occupies a great variety of niches at the understory with diverse crown shapes. Component biomass was highly correlated with tree diameter for both sexes, but the allometries changed with sex and species. Both females and males show consistent patterns of reproductive size dependency. However, the slope of the linear relations differs between the sexes again indicating gender-related differences in reproductive size. According to theory, females allocate more biomass to reproduction than males, which negatively affects their vegetative growth. However, in this study, flowering females did not show less vegetative growth than males suggesting that the two Rhamnus species behave contrary to expectation. Further studies are required to test the assumption that photosynthesis by reproductive organs may substantially contribute to vegetative growth. [ABSTRACT FROM AUTHOR]

In previous research articles on solar drying of Roselle (karkade), statistical analyses on twelve thin-layer drying models proved the superiority of the logarithmic model. This article investigated the effects of the drying conditions on the drying constant (k), coefficients, and drying rate. Validation of the model as well was presented. The rate constant (k) was highly affected by the drying temperature. It increased linearly with the temperature. Air velocity to a lesser extent influenced (k). Coefficient (a) showed a positive relation with both drying-air temperature and velocity. In contrast to coefficient (a), parameter (c) showed an inverse relation with the drying temperature and a moderate dependence on the air velocity. The drying rate was highly influenced by the drying temperature. Raising the temperature increased the drying rate. Furthermore, two criteria were applied to validate the developed model. [ABSTRACT FROM AUTHOR]

We propose a generalized Verlinde formula associated with ( 1, p) logarithmic models of two-dimensional conformal field theories, which have applications in statistical physics problems such as the sand-pile model and phase transitions in polymers. This formula gives the integer structure constants in the whole ( 3p- 1)-dimensional space of vacuum torus amplitudes in which the fusion algebra is a 2p-dimensional subalgebra. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a logarithmic model and Giner’s differential model are compared in the process of simulating the stationary deep-bed drying of a product. The logarithmic model can be interpreted as the propagation of boundary conditions along characteristic lines throughout the bed. This propagation depends on drying air velocity and becomes less pronounced with respect to the depth of the bed. Therefore, transfer time, as defined by the ratio of the depth of the bed to the inlet velocity of drying air is a relevant parameter that can strongly influence the behaviour of the logarithmic model. The logarithmic model is formulated using dimensionless numbers derived from the theory of Transfer Units. The logarithmic model developed in this paper is compared to numerical approximations of the differential model. This comparison has been performed in terms of accuracy (with respect to Giner’s differential model), parsimony (with respect to the number of variables and parameters of the models) and specialization (with respect to the scope of the application fields of the models). Drying conditions ensuring that the logarithmic model describes drying phenomena as well as Giner’s partial differential equation model are determined. The application field of the logarithmic model appears to be more restrictive than Giner’s model, however, it is much more parsimonious than the numerical approximations of the differential model and it may be used by decision-support systems for the design of driers. As an illustration, numerical simulations based on wheat drying are performed. [Copyright &y& Elsevier]

Recent investigations of aeolian transport have focused on increasingly short time scales because of growing recognition that wind unsteadiness is a major factor in the dynamics of sediment transport. However, the statistical reliability of shear velocity (u*) estimates becomes increasingly uncertain as averaging interval is decreased. This study provides an empirical assessment of the influence of averaging interval on the reliability of u* estimates. The data consist of 15-min wind-speed profiles (1 Hz sampling) collected at four coastal sites. Each profile was subdivided into progressively shorter fixed-length time intervals, and estimates of u* and the 95% confidence interval for u* were determined for each time-block using standard statistical techniques.The logarithmic model accurately represents the measured wind-speed profiles, even with relatively brief averaging intervals. Mean r2 values remain robust down to block lengths as short as 10–20 s, typically retaining better than 98% of the r2 value found for the full-length data sets. Fewer than 2% of the individual 10-s blocks had r2 values less than 0.9. However, mean confidence intervals typically expanded by 70–80% of the full-record value as block length decreased from 900 to 10 s. For highly log-linear profiles, this amounted to an absolute increase from about ±8% to only ±14% of u*, so that the additional information gained through the use of shorter averaging intervals may outweigh the increase in statistical uncertainty. Nevertheless, given that rates of aeolian transport are generally modeled as a function of u*3, this increase in uncertainty may be significant for transport modeling. Thus, very short averaging intervals should be used with caution when predicting aeolian sediment flux. It is proposed that transport modeling should incorporate the shear velocity confidence interval as an indicator of the potential error associated with this source of uncertainty. [Copyright &y& Elsevier]

Soil samples were taken from different soil depth of different forest soil story in the Natural Reserve of Changbai Mountain, and their saturated conductivity and effective porosity were measured. The variation of saturated conductivity and effective porosity with different soil depth were studied by regressive analysis and a logarithmic model. The results were compared with the exponential model (Beven 1982). The results of comparison showed that the logarithmic model was more accurate and reasonable than the exponential model for forest catchment. [ABSTRACT FROM AUTHOR]