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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.

The triangular γ-graphyne structure is highlighted in particular, as it is a new configuration with possible applications in medicine. We shed light on this structure’s special qualities and potential uses in healthcare by computing several topological indices linked to it through computational research. Furthermore, we use Shannon’s entropy measure to express the information content of the connection-based topological indices in tandem. This method offers a thorough comprehension of the intricate features and structural properties of the triangular γ-graphyne structure. A logarithmic regression model is built to establish a quantifiable relationship between the computed indices and entropy. The SPSS program was used in the development of this model, allowing for a thorough examination of the relationship between structural features and informational entropy. A regression model based on triangular graphyne topological indices is used as a predictive tool for entropy estimation. [ABSTRACT FROM AUTHOR]

Ensuring site diversity is a fade mitigation technique used to overcome severe rain-induced attenuation encountered in high-frequency signal communications, especially over tropical regions. However, due to the high cost of deploying diverse equipment and terrestrial infrastructures for connections between two sites, gain prediction models are widely used to evaluate the performance of a given scheme. This article presents a study on the behavior of four parameters that contribute to site diversity gain: site separation distance, link frequency, elevation angle, and baseline orientation angle. A correlation between gain and distance is found in the form of an attenuation-dependent logarithmic function instead of the gain being saturated when the increment of the distance exceeds the convective rain cell extent, as was found in the literature. Therefore, a site diversity logarithmic model, SDLog, is proposed. The analysis utilized seven site diversity experiments conducted in the tropics, i.e., in Malaysia, Singapore, and Guam, USA. The performance of the SDLog model was compared with the performances of existing models, namely, the ITU-R, Hodge, Panagopoulos, Semire, and X. Yeo models. The SDLog model reproduces the experimental datasets with an average root mean squared error (RMSE) of 0.12, while those of the ITU-R, Hodge, Panagopoulos, Semire, and X. Yeo models are 0.226, 0.289, 0.19, 0.192, and 0.311, respectively. These models were also evaluated based on diversity experiments conducted in Indonesia, separate from the former evaluation. The RMSE and mean absolute percentage error (MAPE) of all evaluations are presented, and the SDLog model seems to be convincing.

We reflect real spectra of new logarithmic model PT-symmetry operators with singular and non-singular in nature. We also notice the iso-spectral nature between inverted and noninverted PT-symmetry potentials. Present numerical result give good agreement with previous results. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to investigate the predictive importance of the previously validated log(ER)*log(PgR)/Ki-67 predictive model in a larger patient population., Methods: Patients with hormone receptor positive/HER-2 negative and clinical node positive before chemotherapy were included. Log(ER)*log(PgR)/Ki-67 values of the patients were determined, and the ideal cutoff value was calculated using a receiver operating characteristic curve analysis. It was analyzed with a logistic regression model along with other clinical and pathological characteristics., Results: A total of 181 patients were included in the study. The ideal cutoff value for pathological response was 0.12 (area under the curve=0.585, p=0.032). In the univariate analysis, no statistical correlation was observed between luminal subtype (p=0.294), histological type (p=0.238), clinical t-stage (p=0.927), progesterone receptor level (p=0.261), Ki-67 cutoff value (p=0.425), and pathological complete response. There was a positive relationship between numerical increase in age and residual disease. As the grade of the patients increased, the probability of residual disease decreased. Patients with log(ER)*log(PgR)/Ki-67 above 0.12 had an approximately threefold increased risk of residual disease when compared to patients with 0.12 and below (odds ratio: 3.17, 95% confidence interval: 1.48-6.75, p=0.003). When age, grade, and logarithmic formula were assessed together, the logarithmic formula maintained its statistical significance (odds ratio: 2.47, 95% confidence interval: 1.07-5.69, p=0.034)., Conclusion: In hormone receptor-positive breast cancer patients receiving neoadjuvant chemotherapy, the logarithmic model has been shown in a larger patient population to be an inexpensive, easy, and rapidly applicable predictive marker that can be used to predict response.

Indonesia’s pulpwood export has shown an increasing trend since 1990s. Along with Brazil, Canada, USA and Chile, Indonesia became one of the top five pulpwood exporter countries in the world. Indonesia’s pulpwood was traded mainly to some Asian countries. This paper examines Indonesian pulpwood export demand during the period 1994-2014 using a Transcendental Logarithmic (TL) model with Seemingly Unrelated Regression (SUR) estimation. Export data from the five top exporter countries in four different markets (China, Korea, Japan and the world) were analysed. The important findings are as follow: firstly, logarithmic income and second order logarithmic income significantly influence the Chinese and Korean markets. Secondly, in general, Indonesia’s own-prices are elastic and have negative signs (-2.308, -1.06 and -2.04 in the Korean, Japanese and the world markets, respectively). Thirdly, due to its positive sign of crossprice elasticity and also positive signs of income elasticity (1.002, 1.722 and 0.625 in the Chinese, Korean and the world markets, respectively), Indonesian pulpwood could be categorized as a substitute and normal goods. Lastly, regarding to negative and elastic Indonesia’s pulpwood own-prices, one possible policy that could be applied by the Government of Indonesia (GoI) is giving a subsidy to reduce pulpwood price by 10%. Subsidy could be implemented by reducing tax and retribution such as property tax (Pajak Bumi dan Bangunan) and local retribution (Retribusi Daerah). By doing so, it would give more benefit in the Korean market compared with other markets. Indonesia’s share of demand would increase from 0.28 to 0.31 with high rate of return (>2). On the world markets, Indonesia’s share of demand would increase from 0.08 to 0.1 with a return rate of 1.89. This study, therefore, suggests that a subsidy policy should be implemented for pulpwood industry in Indonesia.

In the current world's energy system, natural gas, as a clean energy source, is gradually becoming the main energy source. However, the volume of natural gas demand could be able to be influenced by numerous factors, in order to forecast the future trend of the requirement more precisely, it is necessary to further explore effective driving factors influencing the volume of demand and construct a brand new function model of forecasting natural gas demand. In this study, the relative gray relational degree is used to quantitatively describe the correlation among variables, diagnosing whether there are serious multicollinearity problems among these factors. According to the principle of correlation degree: first and then small, a stepwise regression analysis is adopted to refine the analysis of factors influencing natural gas demand. On the basis of statistical significance testing, in-depth studying is conducted on the effective influencing factors of natural gas demand. This helps to develop a more accurate prediction model. In this study, we find that: (1) China's energy consumption structure and economic development influence its natural gas demand. (2) The stepwise regression double logarithmic demand function model based on the effective drivers has good forecasting performance. Researchers can use it to forecast the natural gas demand for medium- and long-term in different regions, and the forecasting results can be considered an important reference basis for scientific natural gas policy formulation. (3) Over the coming 15 years, Natural gas demand in China will continue to grow. It will reach about 6910 × 10 8 m 3 by 2035. It is concluded that the development of China's natural gas demand would be promoted in an increasing scale trend by striving to promote China's high-quality economic development, carefully building a new pattern of high-quality energy development, and increasing the proportion of natural gas consumption. [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.

The purpose of this work is to study the motion of a non-Newtonian fluid in a rock fracture, generated by a constant pressure gradient to which a pulsating component is superposed. The momentum equation is faced analytically by adopting a logarithmic constitutive law; the velocity is expressed as a power series of the amplitude of the pulsating component, up to the second order, easily usable for numerical calculations. The results obtained are compared with those provided in the past by the authors, using a three-parameter Williamson model. The comparison highlights that the value of the mean flow rate in a period differs by less than 10% even if the velocity profiles look quite different.

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 .

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.

Interferometric synthetic aperture radar (InSAR) has become an important technique for studying earthquake cycle deformation. However, due to the limited satellite revisit time, it is often difficult to fully separate coseismic and postseismic slip from InSAR data. Nevertheless, accurately estimating spatiotemporal coseismic and postseismic fault slip distribution models is important for quantifying earthquake slip, understanding the kinematics of seismogenic faulting, and evaluating seismic hazards. Here, we develop a logarithmic model‐based method (LogSIM) for the joint inversion of coseismic and postseismic fault slip using InSAR data from multiple platforms in different orbits. This method considers the nature of early postseismic slip following logarithmic decay. The coseismic slip, the decay time constant, and the decay amplitude of the logarithmic model can be jointly estimated from unwrapped interferograms without the need for InSAR time series calculations, thereby reducing the number of unknown parameters and stabilizing the inversion. The robustness of LogSIM is first validated with synthetic experiments. We then apply LogSIM to invert for the coseismic and postseismic slip models of the 2017 Mw 7.3 Sarpol Zahāb earthquake. We find that the maximum afterslip value and postseismic moment release during the first four months reported in previous studies are underestimated by ~26% and ~31%, respectively, due to the unavailability of the first five days of postseismic deformation. The estimated afterslip distribution spatially overlaps with the aftershocks in the updip region of the fault plane. LogSIM could potentially be extended to integrate different space geodetic data in earthquake cycle deformation modeling. Key Points: LogSIM is an effective and robust tool for coseismic and postseismic fault slip model estimationLogSIM makes use of all available geodetic data to improve the temporal resolution of afterslipLogSIM enables the retrieval of the very early postseismic fault slip [ABSTRACT FROM AUTHOR]

Abstract: Whether Fittsʼ law, a well-known model of human pointing movement, is a logarithmic law or a power law has remained unclear so far. In two widely cited papers, Meyer et al. have claimed that the power model they derived from their celebrated stochastic optimized-submovement theory encompasses the logarithmic model as a limiting case. We show that Meyer et al.ʼs theory implies in fact a quasi-logarithmic, rather than quasi-power model, the two models being not equivalent. [Copyright &y& Elsevier]