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The logistic function is introduced for modeling the compression curves of reconstituted clays over a wide stress range beginning from the initial void ratio. Three parameters in the model can be determined by the least square method based on the measurements. It is found that the compression curves of reconstituted clays can be interpreted very well by the proposed model. The error in the void ratio between the calculations and the measurements varies within a narrow range of ±3%, based on the databases available from our research team and the independent researchers. The proposed model possesses the merit of continuity and derivability without singularity, compared with the conventional piecewise linear method of interpreting the compression behavior. In this study, a simply empirical way is also suggested for determining the compression curves of reconstituted clays in the case without performing consolidation tests. The three parameters in the model are correlated with two physical indices: the void ratio and the void ratio at the liquid limit. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature 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.)

As water allocation (WA) is reduced due to water scarcity in a river basin, the benefit function for the domestic water users and the economic sectors declines accordingly. Using the sigmoid-type equation, which is ubiquitous in natural and man-made systems, this study shows that the S-curve behaviour can be seen in a broad range of basin WA scenarios. A questionnaire survey reveals that progressive water supply cutback results in a mild initial hassle but builds up to an elaborate inconvenience and subsequently a diminished shock to the water users. The economic benefit of water consumption based on 8-year data of states in Malaysia shows evidence of the S-curve characteristics where lesser developed states tend to benefit more as water consumption increases. The model allows the sectorial benefit (and impact) level to be approximated as a function of basin water availability. The mathematical quantification, in lieu of qualitative descriptors, is useful as an integral component in water prioritisation and WA decision-making to provide an empirical assessment of optimum basin-wide benefit. HIGHLIGHTS A sigmoid-type mathematical model for water allocation (WA) benefit is introduced.; We show that data of social and economic benefit evaluation fall into the S-curve pattern.; We identify the different zones of the S-curve with descriptive meaning.; The model can be used for the quantitative evaluation of WA benefits in decision-making.;

Background: The mechanical characteristics of bone are crucial for comprehending its functionality and response to different load conditions, which are essential for advancing medical treatments, implants, and prosthetics. By employing mathematical modeling to analyze the mechanical properties of bone, we can assess stress and deformation under both normal and abnormal conditions. This analysis offers valuable perspectives on potential fracture risks, the effects of diseases, and the effectiveness of various treatments. Therefore, researchers are attempting to find an adequate mathematical description of the mechanical properties of bone. Methods: Experimental stress–stretch external loading curves were obtained through investigations of canine vertebrae. The obtained experimental curves were fitted using the SciPy Python library with a slightly modified logistic function (logistic function plus additional const). Results: The resulting coefficient of determination  R 2  (R squared) for most curves was near 0.999, indicating that an appropriate fitting function was selected for the description of the experimental stress–stretch curves. Conclusions: The stress–stretch behavior of canine vertebrae can be described using a logistic function modified by adding additional parameters for the most accurate fitting results. [ABSTRACT FROM AUTHOR]

This paper presents a quantum formulation for classical abstract dynamical systems (ADS), defined by coupled sets of first-order differential equations. They are referred to as "abstract" because their dynamical variables can be of different interrelated natures, not necessarily corresponding to physics, such as populations, socioeconomic variables, behavioral variables, etc. A classical linear Hamiltonian can be derived for ADS by using Dirac's dynamics for singular Hamiltonian systems. Also, a corresponding first-order Schrödinger equation (which involves the existence of a system Planck constant particular of each system) can be derived from this Hamiltonian. However, Madelung's reinterpretation of quantum mechanics, followed by the Bohm and Hiley work, produces no further information about the mathematical formulation of ADS. However, a classical quadratic Hamiltonian can also be derived for ADS, as well as a corresponding second-order Schrödinger equation. In this case, the Madelung reinterpretation of quantum mechanics provides a quantum Hamiltonian that does provide the quantum formulation for ADS, which provides new quantum variables interrelated dynamically with the classical variables. An application case is presented: the one-dimensional autonomous system given by the logistic dynamics. The differences between the classical and the quantum trajectories are highlighted in the context of this application case. [ABSTRACT FROM AUTHOR]

The diffusion of innovation in the area of banking and certain financial services is a topic eagerly taken up in empirical research, but in the case of Poland the study of this phenomenon in the area of finance and banking that utilizes the econometric models in question is practically non-existent. This article attempts to fill this research gap to some extent. The main objective of the article is to present a comparative analysis of the rate of mobile technologies diffusion among 3 groups of enterprises in the financial and insurance sectors: banking and lending, insurance and reinsurance, and brokerage and other activities. The occurrence of diffusion of mobile technologies and its rate was studied with the help of a logistic function. Results show that in companies operating in the insurance industry, the phase of rapid growth of mobile innovation diffusion lasted longer than in other groups of enterprises, but the highest dynamics of mobile innovation diffusion in the first phase of this phenomenon was achieved by companies conducting brokerage and other activities.

In this article, some mathematical properties of (i, x0)-generalized logistic-type function are presented. This four-parameter generalized function can be considered as a statistical phenomenon enhancing more vigorous survival analysis models. Moreover, the behaviors of the relevant parametric functions obtained are examined with graphics using computer programming language Python 3.9. [ABSTRACT FROM AUTHOR]

Advancements in computer capabilities enable predicting process outcomes that earlier could only be assessed after post-process analyses. In aerospace and automotive industries it is important to predict parts properties before their formation from liquid alloys. In this work, the logistic function was used to predict the evaporation rates of the most detrimental impurities, if the temperature of the liquid aluminum alloy was known. Then, parameters of the logistic function were used to determine the transition points where the reaction order was changing. Samples were heated to 610 °C, 660 °C, 710 °C, and 760 °C for one hour, after which the chemical analyses were performed and evaporation rates were calculated for Cd, Hg, Pb and Zn elements. The pressure inside the encapsulated area was maintained at 0.97 kPa. Whereas parameters that define the evaporation rate increase with the temperature increase, the maximum evaporation rates were deduced from the experimental data and fitted into the logistic function. The elemental evaporation in liquid-aluminum alloys is the best defined by the logistic function, since transitions from the first to zero-order-governed evaporation reactions have nonsymmetrical evaporation rate slopes between the lowest and the highest evaporation rate point. [ABSTRACT FROM AUTHOR]

Digital holography is a promising imaging technology. However, there is speckle noise in the reconstructed image of a digital hologram. Speckle degrades the quality of the reconstructed image. Suppression of speckle noise is a challenging problem in digital holography. A novel method is proposed to reduce speckle by a fast logistic adaptive non-local means (LA-NLM) algorithm. In the proposed method, the logistic function is incorporated into the weight calculation of the NLM algorithm to account for multiplicative speckle noise. Filtering parameters are dynamically adjusted according to the statistical property of speckle in the reconstructed image. To enhance computational efficiency, the proposed algorithm takes advantage of the integral image technique to speed up the calculation of the similarity between image patches. Simulated and experimental digital holograms are obtained to verify the proposed method. The results show that the speckle noise is effectively suppressed in digital holography. The proposed method is efficient and feasible, and can be applied to such fields as three-dimensional display, holographic measurement, and medical diagnosis. [ABSTRACT FROM AUTHOR]

To address the challenge of wind turbines meeting primary frequency regulation requirements, incorporating energy storage devices to handle most of the frequency regulation tasks would result in increased operational costs. When a wind turbine rotor accelerates, it deviates from the maximum power tracking point (MPPT), leading to reduced output while retaining significant rotational kinetic energy. Based on this characteristic, a primary frequency regulation strategy is proposed that coordinates the rotor kinetic energy of a double-fed induction generator (DFIG) with supercapacitors (SCs). Supercapacitors provide power support during low-frequency conditions, while accelerating the wind turbine rotor reduces output during high-frequency conditions. Additionally, continuous attention is given to subsequent frequency changes. In case of short-term, low-frequency conditions, stored kinetic energy is released for power support, establishing a mechanism for wind turbine kinetic energy recovery and release. This mechanism reduces charging and discharging requirements for supercapacitors, extends their service life, and considers both wind turbine frequency regulation requirements and economy. Finally, using MATLAB 2020/Simulink platform allows for the verification of the effectiveness and rationality of this proposed method. [ABSTRACT FROM AUTHOR]

The diffusion of innovation in the area of banking and certain financial services is a topic eagerly taken up in empirical research, but in the case of Poland the study of this phenomenon in the area of finance and banking that utilizes the econometric models in question is practically non-existent. This article attempts to fill this research gap to some extent. The main objective of the article is to present a comparative analysis of the rate of mobile technologies diffusion among 3 groups of enterprises in the financial and insurance sectors: banking and lending, insurance and reinsurance, and brokerage and other activities. The occurrence of diffusion of mobile technologies and its rate was studied with the help of a logistic function. Results show that in companies operating in the insurance industry, the phase of rapid growth of mobile innovation diffusion lasted longer than in other groups of enterprises, but the highest dynamics of mobile innovation diffusion in the first phase of this phenomenon was achieved by companies conducting brokerage and other activities. [ABSTRACT FROM AUTHOR]

Patterns of linear and weight growth during the first year of life in saffron cod from different parts of the range are considered based on measurement data and summarized materials for the species available in literature. The growth within a year is simulated by a logistic function. Individuals from the southern areas of the range have been found to grow significantly faster. The most intense linear growth is observed in the first 4 months of life when body length in larvae and juveniles increases by about 50–60% per month. Then, the relative increment decreases significantly in autumn and winter to a rate no greater than 1–4% by the end of the first year of life. The absolute length increments increase during the first half of the year, reaching a maximum of 25–30 mm per month in late summer and early autumn. In the rest of the year, the absolute increments decrease to 2–3 mm. The weight growth pattern is markedly different from the linear one. The weight growth is most intense and significantly exceeds the linear growth within the first 5 months of life. In this period, the body weight at least doubles each month. The rate of relative increments gradually decreases from the first to the fifth month of life. Afterwards, upon reaching a weight of approximately 5 g, the rate of relative weight increments decreases abruptly, although the absolute increments increase substantially. The maximum weight increments (5–6 g) occur in the sixth–eighth months of life, when fish reach a body length greater than 86 mm. In this short period, the saffron cod gain slightly more than 60% of their first-year weight. The peaks of linear and weight increments are discordant in time, with the lag between them being about 2 months. The maximum body length increments are observed in July–September; the maximum weight increments occurred in September–November. [ABSTRACT FROM AUTHOR]

SBAS InSAR has long been used to monitor the mining surface deformation, and its research has been of great interest to researchers worldwide. For the unsatisfactory accuracy of SBAS InSAR-monitored mining surface deformation results, a new corrected model is proposed by integrating SBAS InSAR and Logistic Function. Firstly, the time series deformation results of the mining area were obtained by SBAS InSAR, and the variation law of the differences between SBAS InSAR- and leveling-monitored deformation values was statistically analyzed. Subsequently, the corrected model was constructed using the logistic linear regression analysis function and solved using the Levenberg-Marquardt algorithm. Finally, the corrected high-precision time series deformation results in the mining area were obtained. A mining area in Shandong Province of China was taken as the research object, and the practical application effect of the proposed corrected model was verified. Results showed that the Logistic Function could describe the variation law of the differences relatively accurately, and the corrected results were significantly better than the SBAS InSAR-monitored results, and the RMSEs of the corrected results were improved by 33–58%. The accuracy of SBAS InSAR-monitored mining surface deformation was effectively improved. [ABSTRACT FROM AUTHOR]

A typical feature of life cycles of rock bands is that they seem to consist of two distinct stages. A first stage associates with initial entry and a second stage seems to be related to more mainstream success. This paper proposes a simple model to describe these two stages in the life cycles. The model is put to an empirical test by analyzing the numbers of annual shows of forty-nine heavy metal bands. It is found that initial peak success is attained, on average, after seven years, and that the second wave of success occurs after twenty years, again on average. The second peak associates with twice as much success as the first. [ABSTRACT FROM AUTHOR]

Background: The mechanical characteristics of bone are crucial for comprehending its functionality and response to different load conditions, which are essential for advancing medical treatments, implants, and prosthetics. By employing mathematical modeling to analyze the mechanical properties of bone, we can assess stress and deformation under both normal and abnormal conditions. This analysis offers valuable perspectives on potential fracture risks, the effects of diseases, and the effectiveness of various treatments. Therefore, researchers are attempting to find an adequate mathematical description of the mechanical properties of bone. Methods: Experimental stress–stretch external loading curves were obtained through investigations of canine vertebrae. The obtained experimental curves were fitted using the SciPy Python library with a slightly modified logistic function (logistic function plus additional const). Results: The resulting coefficient of determination R2 (R squared) for most curves was near 0.999, indicating that an appropriate fitting function was selected for the description of the experimental stress–stretch curves. Conclusions: The stress–stretch behavior of canine vertebrae can be described using a logistic function modified by adding additional parameters for the most accurate fitting results.

Magnetic and gravity anomalies have spatially overlapping fingerprints from many buried sources that differ in shape, depth, density contrast, magnetization intensity, and direction. Geophysicists have developed a suite of image enhancement filter algorithms that accurately represent the geometry and detail of subsurface features. Edge enhancement filters are high-pass filters that emphasize potential field anomalies, horizontal lateral edges, and the horizontal location of buried sources, i.e., specific combinations of directional derivatives of gravity and magnetic fields. Lateral edge enhancement filters (e.g., THG, AS, TA, TM, LTHG, IL, and ILTHG) were investigated using Gaussian noise on synthetic magnetic and gravity field data. The results show that LTHG and IL perform better than the other procedures. The ILTHG filter defined with the logistic function does not have the required accuracy and capability to determine the lateral boundaries. In addition, the filters were examined using real gravity field data from the Western Carpathians area in Slovakia. The primary and secondary faults in the western and southern Tribeč Mountains and the secondary faults and geological formations in the Pohronský Inovec Mountains are recognizable in the LTHG and IL images. The results of the LTHG and IL maps will allow us to improve the qualitative interpretation of gravity anomalies in studying the structural and tectonic geology of the Slovak Tribeč and Pohronský Inovec Mountains. [ABSTRACT FROM AUTHOR]

Artefacts in high‐resolution multidimensional nuclear magnetic resonance (NMR) spectra, known as t1 noise, can significantly downgrade the spectral quality and remain a significant noise source, limiting the sensitivity of most two‐dimensional NMR experiments. In addition to highly sensitive hardware and experimental designs, data post‐processing is a relatively simple and cost‐effective method for suppressing t1 noise. In this study, histograms and quantiles were used to obtain a robust estimation of noise level. We constructed a weighted matrix to suppress the t1 noise. The weighted matrix was calculated from the logistic functions, which were adaptively computed from the spectrum. The proposed method is robust and effective in both simulations and actual experiments. Further, it can maintain the quantitative relationship of the spectrogram and is suitable for various complex peak types. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) 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.)

Analysis of dose-response data is an important step in many scientific disciplines, including but not limited to pharmacology, toxicology, and epidemiology. The R package drda is designed to facilitate the analysis of dose-response data by implementing efficient and accurate functions with a familiar interface. With drda it is possible to fit models by the method of least squares, perform goodness-of-fit tests, and conduct model selection. Compared to other similar packages, drda provides in general more accurate estimates in the least-squares sense. This result is achieved by a smart choice of the starting point in the optimization algorithm and by implementing the Newton method with a trust region with analytical gradients and Hessian matrices. In this article, drda is presented through the description of its methodological components and examples of its user-friendly functions. Performance is evaluated using both synthetic data and a real, large-scale drug sensitivity screening dataset.

The task of projecting the number of inhabitants is based on rigorous calculations, on prior knowledge about the behavior of demographic variables that imply population entries or exits. The objective of this article was to present a statistical method for estimating demographic projections, showing its methodological and technical aspects, so that non-demographers have the necessary elements for their estimation. The above was achieved through the use of the Logistics Function, taking the reader step by step in order to make the procedure to follow clear; Subsequently, for this purpose, a projection exercise is carried out using data for the young population (15 to 29 years old) in the Metropolitan Zone of the Valley of Mexico (ZMVM) as an example. The results show that in 2050 the number of young people in the ZMVM will reach 5.2 million, which corresponds to a decrease of 0.76%. This indicates that, within approximately 30 years, the number of young people in the ZMVM will still be high (22.27% of the total population of said area). It is concluded that by using the logistic function an acceptable estimate can be obtained in population projections, in general and by large age groups.

Digital holography is a promising imaging technology. However, there is speckle noise in the reconstructed image of a digital hologram. Speckle degrades the quality of the reconstructed image. Suppression of speckle noise is a challenging problem in digital holography. A novel method is proposed to reduce speckle by a fast logistic adaptive non-local means (LA-NLM) algorithm. In the proposed method, the logistic function is incorporated into the weight calculation of the NLM algorithm to account for multiplicative speckle noise. Filtering parameters are dynamically adjusted according to the statistical property of speckle in the reconstructed image. To enhance computational efficiency, the proposed algorithm takes advantage of the integral image technique to speed up the calculation of the similarity between image patches. Simulated and experimental digital holograms are obtained to verify the proposed method. The results show that the speckle noise is effectively suppressed in digital holography. The proposed method is efficient and feasible, and can be applied to such fields as three-dimensional display, holographic measurement, and medical diagnosis.

Advancements in computer capabilities enable predicting process outcomes that earlier could only be assessed after post-process analyses. In aerospace and automotive industries it is important to predict parts properties before their formation from liquid alloys. In this work, the logistic function was used to predict the evaporation rates of the most detrimental impurities, if the temperature of the liquid aluminum alloy was known. Then, parameters of the logistic function were used to determine the transition points where the reaction order was changing. Samples were heated to 610 °C, 660 °C, 710 °C, and 760 °C for one hour, after which the chemical analyses were performed and evaporation rates were calculated for Cd, Hg, Pb and Zn elements. The pressure inside the encapsulated area was maintained at 0.97 kPa. Whereas parameters that define the evaporation rate increase with the temperature increase, the maximum evaporation rates were deduced from the experimental data and fitted into the logistic function. The elemental evaporation in liquid-aluminum alloys is the best defined by the logistic function, since transitions from the first to zero-order-governed evaporation reactions have nonsymmetrical evaporation rate slopes between the lowest and the highest evaporation rate point.

To address the challenge of wind turbines meeting primary frequency regulation requirements, incorporating energy storage devices to handle most of the frequency regulation tasks would result in increased operational costs. When a wind turbine rotor accelerates, it deviates from the maximum power tracking point (MPPT), leading to reduced output while retaining significant rotational kinetic energy. Based on this characteristic, a primary frequency regulation strategy is proposed that coordinates the rotor kinetic energy of a double-fed induction generator (DFIG) with supercapacitors (SCs). Supercapacitors provide power support during low-frequency conditions, while accelerating the wind turbine rotor reduces output during high-frequency conditions. Additionally, continuous attention is given to subsequent frequency changes. In case of short-term, low-frequency conditions, stored kinetic energy is released for power support, establishing a mechanism for wind turbine kinetic energy recovery and release. This mechanism reduces charging and discharging requirements for supercapacitors, extends their service life, and considers both wind turbine frequency regulation requirements and economy. Finally, using MATLAB 2020/Simulink platform allows for the verification of the effectiveness and rationality of this proposed method.

Given the increasing growth of the Web and consequently the growth of e-commerce, the application of recommendation systems becomes more and more extensive. A good recommendation algorithm can provide a better user experience. In the collaborative filtering algorithm recommendation system, many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings, this paper proposes an improved constrained Bayesian probability matrix factorization algorithm. The algorithm introduces a potential similarity constraint matrix for specific sparsely scored users to affect the user's feature vector, and uses the Logistic function to express the nonlinear relationship of the potential factors, combined with the Markov chain Monte Carlo method for training. Finally, the data set is used for testing and comparative evaluation. This experiment proves that the algorithmic model can be efficiently trained using Markov chain Monte Carlo methods by applying them to the MovieLens and Netflix dataset. The experimental results show that the algorithm has better predictive performance and is suitable for solving the problem of sparse rating matrix of specific users. [ABSTRACT FROM AUTHOR]

How to generate slip surfaces efficiently is crucial for the stability analysis of slopes. Traditional approaches for generating slip surfaces rely directly on the geometric features of slopes, which makes it complicated to generate arbitrary slip surfaces. Moreover, the resulting surfaces are usually not smooth in many cases. This study proposes a method for generating an arbitrary slip surface utilizing the superposition of the integral of the logistic function. This technique can transform the traditional geometry problem into a parameter selection one, in which three groups of parameters in the function determine the shape of the slip surface. The higher-order continuity and differentiable property of this function ensure the smoothness of the slip surfaces. The paper investigates the impact of relevant parameters on the slip surface's pattern and provides the upper and lower bounds of the control variables. This method further develops a procedure for producing arbitrary slip surfaces. The factor of safety and the critical slip surface are calculated using the Morgenstern–Price method and the biogeography-based optimization. After constructing the slip surfaces by this method, two cases in Association for Computer Aided Design show that this method is applicable and verify its calculation accuracy by comparing the factor of safety and the critical slip surface. The results indicate that this approach presents a new direction for the slip surface structure, which can be a general method extending from two to three dimensions. [ABSTRACT FROM AUTHOR]

Ductile-to-brittle transition (DBT) characteristics of three steels for reactor pressure vessel (RPV) belt line application are analyzed from new parameters based on model functions describing the strength and toughness characteristics of the materials. In order to estimate nil-ductility temperature (NDT) from strength property, a strain rate-compensated temperature parameter based on the thermally activated deformation of materials is adopted. A measure of NDT is determined from tensile strength properties for the first time assuming an estimated notch tip strain rate at the lower shelf. It is estimated to be 110, 42, and 106 K for the Cr-Mo-V-Ni, 20MnMoNi55, and A533B steels, respectively. The measure of ductile-to-brittle transition temperature (DBTT) in steels using 41-J Charpy impact-absorbed energy on the basis of a logistic class of functions is compared and shown to be equivalent with those obtained from fitting the tanh model equation. A bi-logistic function based on the concept of separable parameters representing the fracture of ductile and brittle zones in steels within the DBTT regime was applied to model the Charpy impact energy behavior of the three steels. The bi-logistic function-fitting parameters yielded a new measure of brittleness as a DBT characteristic of steels that correlated well with other measures of transition temperature of the selected RPV steels. The parameters from the hyperbolic and logistic fitting were used to develop a model relationship suitable for the generation of a master curve based on Charpy energy in exponential form that unifies the transition temperature behavior of the selected western and eastern RPV materials. The model relationship is also found to closely predict ~5 K of the reference temperature To determined as per American Society for Testing and Materials standard E1921 of the selected RPV steels. [ABSTRACT FROM AUTHOR]

The study aimed was to determine the best nonlinear function describing the growth stages of the Japanese quail breed. To this aim, growth functions such as exponential, logistic, von Bertalanffy, Brody, and Gompertz were used as nonlinear functions is used in the description of the body weight-age relationship of male and female Japanese quails. The Multivariate Adaptive Regression Splines (MARS) data mining algorithm was applied to the individual growth parameters obtained from the determined as the best fit model, and the relationship between sex and growth parameters with it has been revealed. The study dataset was 1267 body weight-age records collected from the hatching to the 6th week of age of 181 Japanese quails consisting of 90 females and 91 males. Each model was applied separately for both males and females. Model fit criteria such as coefficient of determination (R2), adjusted coefficient of determination (R2adj), Akaike's information criterion (AIC), and Bayes information criterion (BIC) were used to evaluate the performances of the growth functions used individually. All the statistical analyses were made by the R package program. The growth curve models were ranked in the form of Logistic > Gompertz > von Bertalanffy > Brody > Exponential according to the goodness of fit criteria. The most suitable model among the non-linear models in terms of performance was logistic. When the relationship between the growth curve parameters and body weight of the logistic model was explained with the MARS algorithm, the goodness of fit criteria showed that the obtained MARS model showed reliable performance. In addition, Pearson’s correlation coefficient between real and estimated body weight was found quite strong for the MARS algorithm (r=0.935). The results showed that the MARS algorithm can be presented as a good reference for breeders to establish breed standards and selection strategies for Japanese quails in growth parameters for breeding purposes.

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 Phyllostachys edulis 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. [ABSTRACT FROM AUTHOR]

With the rise in remote work culture and increased computing capabilities of head-mounted displays (HMDs), more immersive, collaborative experiences are desired in remote–local mixed/augmented reality (MR/AR). Photorealistic full-body avatar representations of users in remote workspace interactions have shown to have increased social presence, nonverbal behavior, and engagement. However, a direct mapping of the body pose angles from local to the remote workspace will, in most cases, result in positional errors during human–object interaction, caused by the dissimilarity between remote and local workspaces. Hence, the interaction must be retargeted, but it should be retargeted in such a way that the original intent of the body pose should be preserved. However, these two objectives sometimes contradict each other. As a result, a multi-objective optimization (MO) problem can be formulated where the primary objective is to minimize positional errors and the secondary objective is to preserve the original interaction body pose. The current state-of-the-art solution uses an evolutionary computation-based inverse kinematic (IK) approach to solve the MO problem where the weights between the objectives must be set by the user based on trial and error, leading to a suboptimal solution. In this paper, we present a new dynamic weight allocation approach to this problem, where a user has the flexibility to set a chosen minimum error tolerance, and the weights will be distributed between the objectives based on a dynamic allocation algorithm. We have used a two-pronged approach to test the adaptability and robustness of this mechanism: (i) on motion-captured human animations of varying levels of speeds, error tolerances, redirections and (ii) we conducted an experiment involving 12 human participants and recorded, redirected their actions performed during a book-shelving task in AR. Compared to the static weighting, the dynamic weighted mechanism showed a net (primary + secondary objective) decrease in error ranging from 20.5% to 34.42% across varying animation speeds and a decrease in error ranging from 11.44% to 36.2% for the recorded human actions during the AR task, demonstrating its robustness and better pose preservation across interactions. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for detecting deforestation using Sentinel-1 C-band backscattering data. It is based on the temporal analysis of the backscatter intensity and its correlation with the scattering behavior of deforested plots. The backscatter intensity temporal variability is modeled with a logistic function, whose lower and upper boundaries are, respectively, set based on the representative backscatter values for forest and deforested plots. The approach also enables the identification of the date of each deforestation event, corresponding to the inflection point of the logistic curve that best fits the backscatter intensity time series. The methodology was applied to two forest biomes, a tropical forest at Iguazu National Park in Argentina and a temperate forest in the Brăila region in Romania. The optimal flattening parameter was 0.12 for both sites, with an F1-score of 0.93 and 0.71 for the tropical and temperate forests, respectively. The temporal accuracy shows a bias on the estimated date, with a slight delay of 2 months. The results reveal that the Sentinel C-band data can be successfully used for deforestation detection over tropical forests; however, the accuracy for temperate forests might be 20 pp lower, depending on the environmental conditions, such as rainfall, snow and management after logging. [ABSTRACT FROM AUTHOR]

This study aimed to describe the change in the number of successful nests of the white‐tailed eagle, Haliaeetus albicilla, for 25 years (1997–2021) along the Teshio River (100 km), Japan, which is a new habitat for this endangered species and identify factors driving the number of nests. The number of nests grew from two to nine. The logistic function fitted in well with the growth, and the capacity of the study area sustaining the successful nests was estimated at 6.5. The precipitation in January and April explained the deviation of the observed values from the model prediction. In particular, heavy rain in April was associated with low numbers. Forty‐six nest remains were collected from 17 nest locations. Twelve genera of birds, six genera of mammals, and four genera of fishes were identified. Fish and bird items occupied 93.6% of prey individuals. The fish proportion was similar between high‐performance years when the observed number of successful nests was higher than the model prediction and low‐performance years with a lower number than the prediction (55.2% and 51.0%). However, it was higher in the nests with two fledglings (63.0%) than those with a single fledgling (41.5%). The nearest neighbor distance (NND) of the successful nests declined with the increase in the number of nests. Based on territory size (the mean NND = 7.8 km), the study area can hold 13 nests. The process and mechanism of the dynamics of the number of nests were discussed, focusing on territoriality and weather effects. [ABSTRACT FROM AUTHOR]

In this paper, by introducing well-established epidemiological models, compartmental models and Ising models, examples of their applications in describing epidemics, including some aspects of Covid-19, and several perspectives are presented. The importance of social distancing in preventing the spread of Covid-19 will also be demonstrated. We make a connection between the standard mathematical models employed in epidemics and well-established concepts in condensed matter Physics, such as the Fermi gas and the Fermi-liquid picture. Given the impossibility of making a precise forecast of the disease spread, the importance of taking into account additional factors, such as climate changes and urbanization, in the mathematical description of epidemics are considered.

Commensurate with economic globalization, the demand for mineral resources is increasing. With increased mining activity, problems related to ground subsidence and rock movement are becoming increasingly prominent, even affecting mining production activities. However, the physical mechanisms behind the ground subsidence phenomenon have been poorly studied, especially for metal mines with a steep dip. This paper applies the Interferometric Synthetic Aperture Radar (InSAR) technique and the numerical simulation method to deduce the characteristics of rock movement in the Xinli deposit of the steeply inclined Sanshandao gold mine. The InSAR results indicate that more subsidence has occurred in the southern part of the Xinli Village coastline area than in the northern part. This is also supported by the numerical simulation results obtained by the fast Lagrangian analysis of continua in three dimensions (FLAC3D). Notably, the range of ground subsidence obtained by numerical simulation shows an obvious asymmetry. The monitoring data of the No. 55 prospecting profile offer a plausible explanation for this, as the surrounding rock of the fault’s hanging wall has a wider range of rock movements. Furthermore, the sublevels of the No. 55 prospecting profile at different depths show different rock movement characteristics, and a logistic function can be well applied to the right part of the settlement curve; the parameter “a” in the function formula is very close to the maximum subsidence value for each sublevel. We defined the ratio “r” to measure the difference between the maximum subsidence value and the corresponding parameter “a” and found this value to be positively correlated with the fractal dimension value of deeper sublevels (−320 m, −400 m, −480 m, and −600 m) and negatively correlated with the fractal dimension value of lower sublevels (−200 m and −240 m).

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A constitutive relation for shape memory alloys (SMAs) that is simple, accurate, and effective is the basis for deep-sea intelligent actuators used in marine engineering applications. The existing kinetic models of phase transition all have common drawbacks, such as sharp change at the turning point of the phase transition, constant phase transition rate, and many variable parameters. In this study, the one-dimensional thermodynamic constitutive equation for SMAs is extended based on the thermodynamic framework of the Boyd–Lagoudas constitutive model. In addition, the traditional phase transition function is replaced by an improved logistic nonlinear function in order to construct the relation for the macroscopic variable-speed phase transition that constitutes deep-sea actuator driving wires. The logistic model is compared to other models and verified by the numerical fitting results of the traditional constitutive model and the experimental data for two scenarios: (1) constant load and (2) constant temperature. The results show that the improved constitutive model has more advantages and better adaptability than the traditional models. Consequently, it can accurately describe the slow and gradual phase transitions in the initial and final regions of the phase transition with fewer variable parameters and has the ability to flexibly adjust the rate of change of the phase transition rate. These results provide important theoretical support for the design of SMA deep-sea actuators used in marine engineering applications. [ABSTRACT FROM AUTHOR]

Long-non-coding RNA (lncRNA) is a transcription product that exerts its biological functions through a variety of mechanisms. The occurrence and development of a series of human diseases are closely related to abnormal expression levels of lncRNAs. Scientists have developed many computational models to identify the lncRNA-disease associations (LDAs). However, many potential LDAs are still unknown. In this paper, a novel method, namely MSF-UBRW (multiple similarities fusion based on unbalanced bi-random walk), is designed to explore new LDAs. First, two similarities (functional similarity and Gaussian Interaction Profile kernel similarity) of lncRNAs are calculated and fused linearly, also for disease data. Then, the known association matrix is preprocessed. Next, the linear neighbor similarities of lncRNAs and diseases are calculated, respectively. After that, the potential associations are predicted based on unbalanced bi-random walk. The fusion of multiple similarities improves the prediction performance of MSF-UBRW to a large extent. Finally, the prediction ability of the MSF-UBRW algorithm is measured by two statistical methods, leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The AUCs of 0.9391 in LOOCV and 0.9183 (± 0.0054) in 5-fold CV confirmed the reliable prediction ability of the MSF-UBRW method. Case studies of three common diseases also show that the MSF-UBRW method can infer new LDAs effectively. [ABSTRACT FROM AUTHOR]

As the proportion of sensitive loads in the power grid increases, power users have higher and higher requirements for power quality, and the impact of voltage swells on sensitive loads cannot be ignored. Comprehensively considering the influence of voltage swell amplitude and duration on sensitive loads, based on the characteristics of information technology industry council(ITIC) voltage swell tolerance curve and the monotonicity and continuity of logistic function, the key points of the tolerance curve were used to determine the control variables of the influence degree of voltage swell amplitude and duration, and a method was proposed for evaluating the influence of voltage swell based on sensitive loads. Finally, considering the influence of relay protection configuration and grid structure and fault location on voltage swell duration, different scenarios were set up in the example and the method proposed was used to evaluate the influence degree of the voltage swell of the sensitive load, which verifies the rationality and feasibility of the method, and provides guidance for the evaluation and management of the voltage swell of the sensitive load. [ABSTRACT FROM AUTHOR]

This study combines data-driven-based logistic functions with prediction–area (P–A) plot for delineating target areas of orogenic Au deposits in the eastern margin of the Qinling metallogenic belt, central China. First, appropriate geological and geochemical factors were identified, optimized, and transformed into a series of fuzzy numbers with a range of 0–1 through a data-driven-based logistic function in order to determine the evidence layer for prospecting orogenic Au. In addition, the P–A plot was derived on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme, in the process of which acidic magmatic activity proved to be the most important factor of ore-controlling. Moreover, to further prove the advantages of this method, a traditional linear knowledge-driven approach was carried out for comparative purposes. Finally, based on concentration–area (C–A) fractal theory, the fractal thresholds were determined and a mineral prospecting map was generated. The obtained prediction map consisted of high, medium, low, and weak metallogenic potential areas, accounting for 2.5%, 16.1%, 38.4%, and 43% of the study area, containing 2, 3, 1, and 0 of the 6 known mine occurrences contained, respectively. The P–A plot indicated that the result predicted 83% of Au deposits with 17% of the area, confirming the joint application of the data-driven-based logistic function and P–A plot to be a simple, effective, and low-cost method for mineral prospectivity mapping, that can be a guidance for further work in the study area. [ABSTRACT FROM AUTHOR]

Precise delineation of a salt dome's geobody in seismic data requires intelligent integration, image fusion or combination of seismic attributes using advance methods. There are various attribute integration methods available and many other are still under development. In this study, we introduce a new strategy for feature extraction from seismic images followed by their combination at the data level and subsequent information integration on seismic image. The aim of the presented study was to introduce an efficient method for image segmentation using the ordered weighted averaging (OWA) and the logistic function methods. In other competitive methods, the combination of information is used by employing various weighting functions whereas in the OWA method the specific weights are defined according to the importance of the characteristics of the target under investigation, which can be enhanced in the extracted seismic attributes. Then, the images of seismic attributes are combined by the logistic function method to distinguish the target geobody from the rest of the image. In the logistic function method, the attributes are combined with modified equations with the fuzzy gamma and geometric mean operators. This strategy can define the boundaries of the target and distinguish the geobody in the seismic image. The methodology was applied to synthetic and real field datasets, which contain a salt dome. For comprehensive comparison of the performance of the proposed method with the OWA and the logistic function methods, their various modifications of competitive methods were also applied to the same datasets. The OWA with pessimistic and optimistic weighting algorithms were both applied to fuzzy and binary models. The modified fuzzy logistic function was also applied to the fuzzy and binary models whereas the modified geometric averaging was applied to the datasets. The results were compared qualitatively and quantitatively. For the synthetic data, a synthetic model was used as the base model for pixel-by-pixel comparison with the true model and the binary models. The accuracy is the ratio of pixels correctly selected as the target in the final binary model to the correct pixels of the target in the true model. For the field data example, however, because there was no true model available, an expert interpreted model was used as the base model. The qualitative comparisons of results for the synthetic and field datasets show that the OWA method can better identify the target under investigation in the seismic image. In the quantitative comparison, the OWA pessimistic method presented 99.00% and 94.6% accuracy in the synthetic and real field datasets, respectively. [ABSTRACT FROM AUTHOR]

This paper introduces a spatial queueing model for a single bottleneck during morning peak hours. Utilizing the logistic function and after appropriate calibration, it articulates the arrival and departure flows in continuous, differentiable terms. By validating the model across different peak periods and locations, the demand model's robustness is superior to other commonly used functions. This model also incorporates constant or varying capacity scenarios. It effectively captures key aspects of morning peak traffic, including the emergence of hysteresis loops in fundamental diagrams (FDs) of density and flow. The model's multi-stage approach recognizes three distinct phases in traffic flow: freeflow, transition, and queued segments, ensuring spatial consistency in flow and density across these stages. It accounts for the growth of the queued segment and vehicle spillback under various bottleneck intensities, with the resulting FDs for speed and density also displaying hysteresis loops. The calibration of model parameters utilizes time-series data of traffic flow and density space–time maps derived from real-world data. The validation results accurately reflect real traffic scenarios, emulating the counterclockwise hysteresis loops observed in density and its heterogeneity, and provide both planar and three-dimensional FDs at different points along the traffic link, each mirroring real-life traffic patterns. Additionally, a comparison with the cell transmission model (CTM) reveals that the proposed model exhibits superior generalization and robustness. • Accurate capture of peak traffic patterns with logistic model. • Spatially continuous multi-stage queueing approach. • Precise replication of hysteresis loops. • Concise model with superior performance to benchmarks. [ABSTRACT FROM AUTHOR]

Antibodies constitute a major component of serum on protein mass basis. We also know that the structural diversity of these antibodies exceeds that of all other proteins in the body and they react with an immense number of molecular targets. What we still cannot quantitatively describe is how antibody abundance is related to affinity, specificity, and cross reactivity. This ignorance has important practical consequences: we also do not have proper biochemical units for characterizing polyclonal serum antibody binding. The solution requires both a theoretical foundation, a physical model of the system, and technology for the experimental confirmation of theory. Here we argue that the quantitative characterization of interactions between serum antibodies and their targets requires systems-level physical chemistry approach and generates results that should help create maps of antibody binding landscape. [ABSTRACT FROM AUTHOR]

This paper seeks to establish a framework which operationalizes cognitive traits as a portion of the predefined mastery level, the highest level expected to successfully perform all of the relevant tasks of the target trait. This perspective allows us to use and interpret the cognitive trait levels in relative quantities (e.g., %s) of the mastery level instead of relative standings (i.e., rankings) on an unbounded continuum. To facilitate the proposed perspective, this paper presents an analytical framework that has support on the [0, 1] trait continuum with truncated logistic link functions. The framework provides a solution to cope with the chronic question of "relative standings or magnitudes of learning outcome?" in measuring cognitive traits. The proposed framework is articulated relative to the traditional models and is illustrated with both simulated and empirical datasets within the Bayesian framework, estimated with the Markov chain Monte Carlo method. [ABSTRACT FROM AUTHOR]

In topological dynamical systems (TDS), recurrence (periodic--like recurrence) is one of the important concepts in its studies but one major problem is the inability to demonstrate and/or illustrate its formation in the orbit structure of system from a topological point of view. In this paper, the logistic function was applied to demonstrate the periodic point as a recurrent formation (periodic--like recurrence) in the topological dynamical system and dynamical system. The Wolfram Alpha computational knowledge engine was used in obtaining the tables and the figures for the study through various examples of the logistic function. The study shows that period-2 recurrence is formed when the trajectory of the function is made up of two different values that keep repeating after successive iterations as a result of the period -2 orbits when the parameter of the function is between 3 and 3.45. The study again shows that when the parameter of the function is greater than 3.83 there is a period -3 point hence the formation of other periodic points. Convincingly, beyond this period -3 is another subsequent period called the period-doubling cascade leading into chaos. This period-doubling asserts that other periodic -- like recurrences are also present, hence period -n recurrent exists. [ABSTRACT FROM AUTHOR]

A correct assessment of microalgae growth on porous building materials (i.e.: fired bricks, sandstones and limestones) can provide a useful tool for researchers and practitioners. In fact, it may help predicting the biofouling damage extension and it can assist the experts in a correct planning of maintenance interventions to limit costs. The literature regarding such issue outlined the Avrami’s model as the most recurrent one, even considering the influence of biocidal treatments on the substrate. However, it seems to have some limitations when the growth is very fast or, conversely, when the latency time is extended over the time. Therefore, a different modelling approach is here proposed, by using the logistic function (extensively used i.e. in population growth). Results reveal that the logistic function seems to succeed in better modelling the available experimental data. Moreover, it seems to overcome the limits of the Avrami’s model, as well as to be less influenced by the main drivers of microalgae growth, such as porosity and roughness of the substrate, biocides treatments and environmental conditions (temperature). [ABSTRACT FROM AUTHOR]