In regions with seasonal frozen soil, mechanical properties of soil are impacted by freeze-thaw cycles, which influence the shear resistance of soil-concrete interface in geotechnical engineering. ...For evaluating shear properties at the soil-concrete interface, freeze-thaw cycles and direct shear experiments were conducted in this research. The stress-displacement curves, shear strength and parameters of the interface were analyzed in relation to freeze-thaw cycles, while the influences by moisture contents and normal stresses were considered. Results show that the curves related to shear stresses and displacements at the interface are strain-hardening, and shear properties gradually deteriorate with repetitive freezing and thawing. The shear strength is positively related to normal stresses, and it increases by approximately 250% while normal stress varies from 100 to 400 kPa. However, it is negatively correlated with growing moisture contents and freeze-thaw cycles. The reduction in shear strength is about 21%–25% after freeze-thaw cycles, along with a decrease in cohesion ranging from 14% to 20% and for angle of internal friction it reaches at 14%–24%. Moreover, an improved hyperbolic model based on the logistic function and hyperbolic model was established to evaluate shear properties at the interface under freeze-thaw cycles, providing a theory base for engineering construction in seasonally frozen soil regions.
•An improved hyperbolic model of silty clay-concrete interface was established.•The logistic function was used to describe the damage of the interface.•The effect of freeze–thaw cycles on the interface was investigated.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
New soliton solutions of fractional Jaulent-Miodek (JM) system are presented via symmetry analysis and fractional logistic function methods. Fractional Lie symmetry analysis is unified with symmetry ...analysis method. Conservation laws of the system are used to obtain new conserved vectors. Numerical simulations of the JM equations and efficiency of the methods are presented. These solutions might be imperative and significant for the explanation of some practical physical phenomena. The results show that present methods are powerful, competitive, reliable, and easy to implement for the nonlinear fractional differential equations.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The boundaries detection techniques have a great role in enhancing and interpreting the geologic features of magnetic data. In the literature, several filters (THG, AS, TA, NTilt, Theta, TDX, TAHG, ...LTHG) for identifying the boundaries of the magnetic sources have been suggested. These methods are generally performed based on gradients (vertical and horizontal) of the potential field. This paper presents a comparative investigation of different boundary detection filters including THG (total horizontal gradient), AS (analytical signal), TA (tilt angle), Theta (Cos θ), TDX (horizontal tilt angle), and LTHG (Logistic function of the THG). The effect of each filter was examined on two synthetic magnetic data sets. Moreover, the filters are also applied to a real magnetic data set from the Gabal (G) Um Monqul, North Eastern Desert (NED) of Egypt. The obtained results were correlated with known geologic structures of the study area. From the comparison between several applied methods, the horizontal boundaries of geologic sources obtained by the LTHG were found to be sharper and clearer than other ones. The results confirm that the LTHG method is an effective filter for interpreting aeromagnetic data qualitatively and can be applied for enhancing the source edges of different potential field datasets.
•We compared the effectiveness of the THG, AS, TA, Theta, TDX and LTHG filters.•The filters were tested on both synthetic and real data.•The results showed that the LTHG is more effective in interpreting magnetic data than other filters.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Wildfires are a major disturbance in the Mediterranean Basin and an ecological factor that constantly alters the landscape. In this context, it is crucial to understand where wildfires are more ...likely to occur as well as the drivers guiding them in complex landscapes such as the Mediterranean area. The objectives of this study are to estimate wildfire probability occurrence as a function of biophysical and human-related drivers, to provide an assessment of the relative impact of each driver and analyze the performance of machine learning techniques compared to traditional regression modeling. By employing an Artificial Neural Network model and fire data (2004–2012), we estimated wildfire probability across two geographical regions covering most of the Italian territory: Alpine and subalpine region and Insular and peninsular region. The high classification accuracy (0.68 for the Alpine and subalpine region and 0.76 for the Insular and peninsular region) and good performances of the technique (AUC values of 0.82 and 0.76, respectively) suggest that our model can be used in the areas studied to assess wildfire probability occurrence. We compared our model with a logistic function, which showed a weaker predictive power (AUC values of 0.78 for the Alpine and subalpine region and 0.65 for the Insular and peninsular region) compared to the Artificial Neural Network. In addition, we assessed the importance of each variable by isolating it in the model. The importance of an individual variable differed between the two regions, underscoring the high diversity of wildfire occurrence drivers in Mediterranean landscapes. Results show that in the Alpine and subalpine region, the presence of forest is the most important variable, while climate resulted as being the most important variable in the Insular and peninsular region. The majority of areas recently affected by large wildfires in both regions have been correctly classified by the ANN model as ‘high fire probability’. Hence, the use of an Artificial Neural Network is efficient and robust for understanding the probability of wildfire occurrence in Italy and other similar complex landscapes.
•Artificial neural network (ANN) for estimating probability of wildfire occurrence.•ANN shows a more robust predictive power than regressive models.•Shrublands was surprisingly less important than the other land cover variables.•Human predictors do not show much importance individually.•The importance of each single driver differs among the study areas.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Several snow-rain phase separation models was evaluated at various synoptic stations of Iran.•A new sigmoidal model was proposed for snow-rain phase separation.•The proposed model performed better ...than the existing models for snow-rain phase discrimination.
This paper presents the use of a double sigmoidal model for precipitation phase partitioning in mountains. For the model development and evaluation, daily rainfall, minimum and maximum air temperatures, and snow depth data from twelve synoptic stations in the mountainous regions of Iran are used. The model performance is compared against six air temperature-based methods using the Taylor diagram, the coefficient of determination (R2), and the Root Mean Square Error (RMSE) performance indicators. Comparing the frequency of snowfall events estimated by the proposed model with those of the existing models and observation at the selected stations showed better performance of the proposed model in precipitation phase discrimination at the studied stations. The average R2 statistic that relates the frequency of snowfall occurrences estimated by the proposed model with the frequency of snowfall events observed at the studied stations is 0.989, which is the highest compared to other available models. Its average error across the stations is 4%, the lowest among the examined models. A split sample approach was used to ensure that the parameters of the proposed model are appropriately estimated. The result showed that the R2 and RMSE values of both calibration and validation subsets are not vastly different from each other, suggesting that the parameters of the two experiments are stable and the corresponding results are robust. The proposed model also performs better than other existing models in accurately estimating air temperature thresholds required for precipitation phase discrimination as well as the snow depth data in all studied stations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•New method to obtain the parameters of the logistic function applied to wind turbine power curves.•The parameters of the logistic function are obtained in a deterministic way and they have technical ...meaning.•Alternative models, based on the logistic function, to the power curve are proposed considering several aproximations.
The current procedure for obtaining the parameters of the logistic function, used as a model for the power curve of wind turbines, provides meaningless values. These values are different for each wind turbine and obtaining them requires an optimization process. This paper proposes a procedure to obtain the parameters of the 4-parameter logistic function based on the features of the power curve, providing a model that is a function of the power curve parameters supplied by the manufacturer. Furthermore, that model can be used to derive another 4-parameter model and a 3-parameter model is proposed for certain conditions. The three models consist of a continuous function which simplifies the implementation of the curve in a computer program compared to piecewise models. In addition, the probability density function of the output power of a wind turbine is derived by using each model.
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Multimodel frameworks are common in contemporary elasmobranch growth literature. These techniques offer a proposed improvement over individual growth functions by incorporating additional candidate ...models with alternative characteristics. Sigmoid functions (e.g. Gompertz and logistic) are a popular alternative to the commonly used von Bertalanffy growth function (VBGF) as they are hypothesized to better suit certain taxa based on body shape (such as batoids) or reproductive mode (such as egg‐layers). However, this hypothesis has never been tested. This study examined 74 elasmobranch multimodel growth studies by comparing the growth curves of their respective candidate models. Hypotheses regarding model performances were rejected as the VBGF was equally likely to fit best for all taxa and reproductive modes. Subsequently, no individual model was suited to be used a priori. Differences between candidate model fits were greatest at age zero with Gompertz and logistic functions providing estimates that were 15% and 23% larger on average than the VBGF, respectively. However, length‐at‐age estimates of the different models became negligible at older ages. Differences between candidate models were mostly small (≤5%), and the multimodel framework only marginally affected length‐at‐age estimates. However, there were cases where some candidate models provided inappropriate fits that contrasted considerably to the best fitting model. In some of these instances, a single‐model framework could have yielded biologically unrealistic growth estimates. Therefore, no study could pre‐empt whether or not it required a multimodel framework. A framework was subsequently recommended to maximize the accuracy of model fits for elasmobranch length‐at‐age estimates using multimodel approaches.
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DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
Height to crown base (HCB) is an important predictor variable for forest growth and yield models and is of great significance for bamboo stem utilization. However, existing HCB models built so far on ...the hierarchically structured data are for arbor forests, and not applied to bamboo forests. Based on the fitting of data acquired from 38 temporary sample plots of
forests in Yixing, Jiangsu Province, we selected the best HCB model (logistic model) from among six basic models and extended it by integrating predictor variables, which involved evaluating the impact of 13 variables on HCB. Block- and sample plot-level random effects were introduced to the extended model to account for nested data structures through mixed-effects modeling. The results showed that bamboo height, diameter at breast height, total basal area of all bamboo individuals with a diameter larger than that of the subject bamboo, and canopy density contributed significantly more to variation in HCB than other variables did. Introducing two-level random effects resulted in a significant improvement in the accuracy of the model. Different sampling strategies were evaluated for response calibration (model localization), and the optimal strategy was identified. The prediction accuracy of the HCB model was substantially improved, with an increase in the number of bamboo samples in the calibration. Based on our findings, we recommend the use of four randomly selected bamboo individuals per sample to provide a compromise between measurement cost, model use efficiency, and prediction accuracy.
Regulated cell death (RCD) encompasses the activation of cellular pathways that initiate and execute a self-dismissal process. RCD occur over a range of stressors doses that overcome pro-survival ...cellular pathways, while higher doses cause excessive damage leading to passive accidental cell death (ACD). Hydrogen peroxide (HP) has been proposed as a potential tool to control harmful cyanobacterial blooms, given its capacity to remove cyanobacterial cells and oxidize cyanotoxins. HP is a source of hydroxyl radicals and is expected to induce RCD only within a limited range of concentrations. This property makes this compound very useful to better understand stress-driven RCD. In this work, we analyzed cell death in microcystin-producing
Microcystis aeruginosa
by means of a stochastic dose response model using a wide range of HP concentrations (0, 0.29, 1.76, 3.67, 7.35, 14.70, and 29.5 mM). We used flow cytometry and unsupervised classification to study cell viability and characterize transitional cell death phenotypes after exposing cells to HP for 48 and 72 h. Non-linear regression was used to fit experimental data to a logistic cumulative distribution function (cdf) and calculate the half maximal effective concentration (EC
50
). The EC
50
of
M. aeruginosa
exposed to HP were 3.77 ± 0.26 mM and 4.26 ± 0.22 mM at 48 and 72 h, respectively. The derivative of cdf (probability density function; pdf) provided theoretical and practical demonstration that EC
50
is the minimal dose required to cause RCD in 50% of cells, therefore maximizing the probability of RCD occurrence. 1.76 mM HP lead to an antioxidant stress response characterized by increased reactive oxygen species (ROS) levels and HP decomposition activity. The exposure of 3.67 mM HP induced a dose-related transition in cell death phenotype, and produced several morphological changes (a less dense stroma, distortion of the cell membrane, partial disintegration of thylakoids, extensive cytoplasmic vacuolation and highly condensed chromatin). The EC
50
and the stochastic cdf and pdf together with the multidimensional transitional phenotypic analysis of single cells contribute to further characterize cell death pathways in cyanobacteria.
The utility function is very significant for solving the real-time pricing problem of smart grid. Based on the Logistic function, a new utility function is constructed to satisfy four properties of ...the utility function. In addition, from the perspective of social welfare, the real-time pricing optimization model of smart grid is established. By using the KKT conditions and the improved Fischer-Burmerister smoothing function, the optimization model is transformed into a smoothing equations problem and the smoothing Newton algorithm is used to obtain the optimal solution of the problem. The nonsingularity of the Jacobian matrix and the global convergence of the algorithm are proved. The simulation results show that, compared with previous quadratic and logarithmic utility functions, the new utility function can not only reduce the user’s electricity consumption and the supplier’s cost can but also improve the user’s utility and the total social welfare, which also indicates that the new utility function is effective in establishing the real-time pricing model of smart grid. Furthermore, the iteration times of several algorithms to solve the real-time pricing problem of smart grid are compared, which showed that the convergence rate of the smoothing Newton algorithm is very fast.
•A new utility function is constructed by the Logistic function.•By the KKT conditions and FB function, the problem is transformed into an equation.•The nonsingularity of Jacobian matrix and the global convergence are proved.•The new function is superior to quadratic and logarithmic functions in tests.•The higher smoothing function’s approximation, the better the convergence effect.
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