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• Products from Polygonum aviculare and Pseudomonas fluorescens with biostimulant activity • Plant extracts with P. fluorescens for treating Melilotus officinalis seed improved germination • Infusions and macerates differed in the concentration of bioactive compounds • Fusion of extracts with bacteria elicits a greater stimulating potential • Mathematical models were developed for the tested seed treatments The empirical evidence indicated that chemicals in plants and selected microorganisms can promote the germination process and determine the growth, viability, survival and yield potential of industrial crop plants. The main objective of this study was exploring the phytochemical and microbiological synergistic activity between plant extracts from Polygonum aviculare L. and beneficial bacteria Pseudomonas fluorescens to enhance germination of Melilotus officinalis seeds and improve morphological characteristics of seedlings. The derived plant preparations were analyzed qualitatively, which showed that the aqueous extracts of P. aviculare were characterized by a higher content of phenolic compounds, micro- and macroelements and carbohydrates compared to the macerates, and that no phytohormones were detected in both the infusions and macerates. Pure macerates (MAC), infusions (IF), bacterial cultures (PF) and their mixtures (MAC+PF; INF+PF) were used for seed treatment of test plants. In the control group, lower values of M. officinalis seed germination and seedling growth rates were observed in relation to the applied treatments. The most effective germination rates were obtained after the application of pure infusions (IF) and their mixture with P. fluorescens (IF+PF), while the highest growth rates (including an almost twofold increase in the length of the nostril shoots compared to the control) were obtained after the application of P. fluorescens inoculum (PF) and the mixture of the infusion and these bacteria (IF+PF). The use of modeling, as a tool for predicting germination efficiency, has been shown to provide screening opportunities for identifying the effectiveness of biostimulant treatments. Thereby, this indicates the need to develop such an approach under field conditions. [ABSTRACT FROM AUTHOR]

The observed time evolution of a population is well approximated by a logistic growth function in many research fields, including oncology, ecology, chemistry, demography, economy, linguistics, and artificial neural networks. Initial growth is exponential, then decelerates as the population approaches its limit size, i.e., the carrying capacity. In mathematical oncology, the tumor carrying capacity has been postulated to be dynamically evolving as the tumor overcomes several evolutionary bottlenecks and, thus, to be patient specific. As the relative tumor-over-carrying capacity ratio may be predictive and prognostic for tumor growth and treatment response dynamics, it is paramount to estimate it from limited clinical data. We show that exploiting the logistic function's rotation symmetry can help estimate the population's growth rate and carry capacity from fewer data points than conventional regression approaches. We test this novel approach against published pan-cancer animal and human breast cancer data, achieving a 30% to 40% reduction in the time at which subsequent data collection is necessary to estimate the logistic growth rate and carrying capacity correctly. These results could improve tumor dynamics forecasting and augment the clinical decision-making process. [ABSTRACT FROM AUTHOR]

Studying the macroscopic‐phenomenological behavior of amorphous polymers at glass transition is often subject to limitations because the ordinary differential equations that describe the material behavior require numerical solution. To avoid these limitations, ad‐hoc‐formulated models of the glass transition have been proposed. However, their scope of application is expected to be limited due to insufficient theoretical foundation. This work establishes a theoretical framework for models that use the logistic function to approximate the macroscopic‐phenomenological behavior of amorphous polymers at glass transition. For this purpose, an exactly‐solvable Riccati equation is derived within thermodynamics with internal state variables. A closed‐form expression in terms of mathematical functions for the temperature derivative of a single internal state variable is the result. This closed‐form expression contains the logistic function thus featuring a continuous transition region centered around a pressure and cooling‐rate dependent transition temperature. Based on comparison of existing models with the exact solution derived from the Riccati equation, generalized models that approximate the thermal expansion coefficient and heat capacity at glass transition are proposed. This work thus demonstrates the validity of the logistic function in glass transition models of amorphous polymers and provides suggestions as to how existing models can be extended in their applicability. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Mining area is a high incidence area of heavy metal pollution in agricultural land. Scientific evaluation of heavy metal pollution risk in mining area is very important for human health and ecological environment. In view of the shortcomings of the current fuzzy comprehensive evaluation method in the selection of membership function and determination of weight, starting from the relationship between heavy metal concentration and exposure hazards, logistic function distribution is selected to replace the commonly used trapezoidal distribution as the membership function. Considering that the soil pollution risk control standard of agricultural land is related to soil pH value, the pH variation coefficient is introduced, and a combined weight determination method combining pH variation coefficient method and pollution multiple method is constructed. The research results of typical mining areas in Daye city show that the average contents of heavy metal elements As, Cd, Cu and Zn in agricultural land in the mining area are higher than the soil background value in Hubei province, which are 3.28,4.35,3.71 and 1.86 times of the background value respectively. As, Cd and Cu are the main pollutants. The improved fuzzy comprehensive evaluation results show that among the 21 sampling points of agricultural land in the mining area, 19 belong to mild pollution and 2 belong to severe pollution. The heavy metal pollution rate of agricultural land in this area is 100%.Safe utilization measures should be taken to strengthen the detection of soil environment and the collaborative detection of agricultural products. Compared with the current fuzzy comprehensive evaluation method, the improved fuzzy comprehensive evaluation improves the selection of membership function and determination of weight, is more sensitive in evaluation classification, considers more possible risks, and provides a reference scheme for reasonable evaluation of heavy metal pollution risk in agricultural land. [ABSTRACT FROM AUTHOR]

The goal of this paper is to determine a mathematical function which most accurately and efficiently models the time-dependent fragility data representing seismic vulnerability for deteriorating bridge pier columns subjected to ground motions rather than the logistic function that has been commonly used for this application while making as many or fewer assumptions. In this paper, three other functions, including the Gompertz function, the Ricker's growth function, and a power function, are considered and compared against the logistic one. All of these functions are analogous to the logistic one with deviations. The deviating characteristics include the location of the inflection point, the existence of inverted symmetry, and the asymptotic nature of the logistic function. As part of the best function determination procedure, the initial step of a general assessment of a bridge column's vulnerability to seismic damage is to employ time-dependent fragility data and fit the data to an appropriate curve. Inaccurately fitting the data could result in incorrect assumptions in the assessment, which could lead to miscalculated hazards and cost inefficiencies. For a proper fit, the model should be simplistic so as to make as few assumptions about reality as possible while still reasonably fitting the data. The standard function used to fit the fragility data is the logistic curve due to its well-studied nature and simplicity, but the logistic function may not be ideal. Key findings reveal that the Gompertz function better fits the fragility data and does not make any more assumptions. The Ricker's growth function and power function do not fit the fragility data well. [ABSTRACT FROM AUTHOR]

Simplicity renders shake flasks ideal for strain selection and substrate optimization in biotechnology. Uncertainty during initial experiments may, however, cause adverse growth conditions and mislead conclusions. Using growth models for online predictions of future biomass (BM) and the arrival of critical events like low dissolved oxygen (DO) levels or when to harvest is hence important to optimize protocols. Established knowledge that unfavorable metabolites of growing microorganisms interfere with the substrate suggests that growth dynamics and, as a consequence, the growth model parameters may vary in the course of an experiment. Predictive monitoring of shake flask cultures will therefore benefit from estimating growth model parameters in an online and adaptive manner. This paper evaluates a newly developed particle filter (PF) which is specifically tailored to the requirements of biotechnological shake flask experiments. By combining stationary accuracy with fast adaptation to change the proposed PF estimates time-varying growth model parameters from iteratively measured BM and DO sensor signals in an optimal manner. Such proposition of inferring time varying parameters of Gompertz and Logistic growth models is to our best knowledge novel and here for the first time assessed for predictive monitoring of Escherichia coli (E. coli) shake flask experiments. Assessments that mimic real-time predictions of BM and DO levels under previously untested growth conditions demonstrate the efficacy of the approach. After allowing for an initialization phase where the PF learns appropriate model parameters, we obtain accurate predictions of future BM and DO levels and important temporal characteristics like when to harvest. Statically parameterized growth models that represent the dynamics of a specific setting will in general provide poor characterizations of the dynamics when we change strain or substrate. The proposed approach is thus an important innovation for scientists working on strain characterization and substrate optimization as providing accurate forecasts will improve reproducibility and efficiency in early-stage bioprocess development. [ABSTRACT FROM AUTHOR]

Low‐voltage power line carrier network has the advantages of wide distribution and easy access. However, the dynamic topology of intelligent distribution network directly affects the reliability of low‐voltage power line carrier communication. In view of this, this article first analyzes the topological structure and business requirements of intelligent distribution network communication system. Then, based on the traditional power line carrier ant colony routing algorithm, the state transition factor and logistic function in the ant colony algorithm are combined to optimize. An ant colony optimization algorithm for power line carrier based on logistic function is proposed. It improves the reliability of the carrier communication route. It is of great significance to the development of power line carrier communication in smart grid. [ABSTRACT FROM AUTHOR]

The behavior of the dynamical orbit of a system by describing it relies on the method used. The paper uses the logistic function to illustrate and describe the fixed point of the periodic–like recurrence as a periodic -1 recurrent. The study is based on Theorem 1:(𝑋, 𝑇)is a fixed (Stationary) recurrent point, iff for all 𝑥 ∈ 𝑋 and an operator 𝑇: 𝑋 → 𝑋 a continuous map and any neighborhood 𝑈 ⊂ 𝑋 then,⋃ ≠ {n ∈ R:Tn (U) ≠ ∅} ∴T n(x) = x ∈ U, n ∈ R, n > 0, Theorem 2: a point x is periodic -1 or fixed point if F(x) = x, Fix(F) = Per1(F) and form a fixed (Stationary) recurrent point T n(x) = x ∈ U, n ∈ R, n > 0 and, Definition 7: a point x ∈ X is said to be recurrent if for any neighborhood U of x, there exists an integer n ≥ 1such that T n(x) ∈ U through the application of the logistic function. The application of the logistic function on the two theorems (Theorem 1 and Theorem 2) and Definition 7 explained that period-1 recurrent only exists when there is the existence of fixed point (periodic orbits) which depends solely on the initial point and the parameter 𝜶 of the logistic function.. [ABSTRACT FROM AUTHOR]

Starting February 2020, COVID-19 was confirmed in 11,946 people worldwide, with a mortality rate of almost 2%. A significant number of epidemic diseases consisting of human Coron- avirus display patterns. In this study, with the benefit of data analytic, we develop regression models and a Susceptible-Infected-Recovered (SIR) model for the contagion to compare the performance of models to predict the number of cases. First, we implement a good understanding of data and perform Exploratory Data Analysis (EDA). Then, we derive parameters of the model from the available data corresponding to the top 4 regions based on the history of infections and the most infected people as of the end of August 2020. Then models are compared, and we recommend further research. [ABSTRACT FROM AUTHOR]

The Accelerator Driven Advanced Nuclear Energy System (ADANES) is currently undergoing research and development (R&D), presenting challenges in cost estimation due to significant uncertainties. Traditional nuclear power cost assessment methods, tailored for mature technologies, lack relevance for advanced systems like ADANES. To address this gap, our study proposes a unique cost analysis approach, dividing ADANES into two stages: the experimental research and development (ERD) stage and the industrial demonstration (ID) stage. Specificlly, in the ERD stage, this study employs a Logistic function-based evaluation method that considers factors such as construction period extension, the proportion of fixed costs, and the upper limit of cost estimated by experts to address potential cost overruns. For the ID stage, this study utilizes a stochastic differential equation (SDE) to account for uncertainties. Monte Carlo simulation is employed to analyze the impact of parameter changes, including construction period extension and acceptable upper limits of cost and duration. Results reveal a substantial increase in expected cost during the ERD stage, ranging from 100% to 140% of the original budget when extending the experimental research duration by 10% to 50%. The ID stage demonstrates an even more significant impact, with a 50% construction period extension resulting in an expected cost of 182% of the original budget. The study suggests that judiciously extending acceptable cost and duration caps can enhance the project's success rate. This innovative cost analysis approach provides valuable insights for navigating the uncertainties associated with ADANES development. • A cost analysis approach is introduced for the disruptive ADANES nuclear energy system. • It employs a logistic function-based evaluation method and stochastic differential equation to account for uncertainties. • A case study validates the method, revealing the numerical relationship between time extension and cost overrun of ADANES. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

One of the most basic topics in rock mechanic is the shear strength criteria for rock joints. Thus, it is of high importance to accurately predict the shear strength of rock joints. In this study, the abilities for accuracy and agreement of Kriging model-based nonlinear interpolation strategy are investigated in terms of predicting the shear strength of rock joints. Totally 84 datasets were used to construct the Kriging models; the datasets were divided into two main parts: training and testing. The prepared database was applied to the training phase in the Kriging model; this way, several nonlinear basic functions were introduced to enhance the predictions of the Kriging model. The examined functions in this paper were linear, 2-order, 3-order, exponential, logarithmic, logistic, hyperbolic tangent, and hyperbolic sine. The sigmoid forms of the basic functions, including logistic and hyperbolic tangent, provide the superior predictions compared to other mathematical functions, while the 2-order and 3-order forms provide the worst performances than the linear, exponential, and logarithmic functions. According to the obtained results, the logistic-based model with coefficient of determination (R2) of 0.916 was found the optimal model that can be successfully applied to estimating the shear strength of rock joints. [ABSTRACT FROM AUTHOR]

Introduction. There are a few statistical approaches to estimate health impacts of the ambient air pollution concentrations. Air health effects are often studied in short-term exposure. In this context two main techniques are used; time-series and casecrossover (CC). This work focuses on the CC methodology. In the standard method risk is estimated using log-linear models. Aim. This work proposes other types of the models. Material and methods. The CC design is applied with various transformations of air pollution concentration. The mortality data are used for the period from 1987 to 2015 for Toronto, Canada. Daily concentration level of ambient ozone is considered as an exposure. The ozone concentration is transformed and used in the statistical models. The transformation is a product of two parts; a simple function such as logarithm and a logistic function as a weight. The transformed concentration is used in the CC statistical models. The models estimate the coefficient related to transformed concentration. It allows to construct the concentrationresponse function. The generated models are assessed using the Akaike information criterion (AIC). Results. The relative risks (RR), reported at 75th percentile of the concentration (55 ppb) are different. The standard CC model gives RR=1.0195 with the 95% confidence interval (1.0035, 1.0358), whereas the model with the transformation gives better fit and estimates RR=1.0054 (1.0026, 1.0082). Conclusions. The proposed methodology allows to construct an accurate approximation of the concentration-response functions. These functions provide adequate approximations and also identify a potential threshold. [ABSTRACT FROM AUTHOR]

The wind turbine power curve (WTPC) is a mathematical model built to capture the input-output relationship between the generated electrical power and the wind speed. An adequately fitted WTPC aids in wind energy assessment and prediction since the actual power curve will differ from that provided by the manufacturer due to a variety of reasons, such as the topography of the wind farm, equipment aging, and multiple system faults. As such, this paper introduces a novel approach for WTPC modeling that combines Gaussian process (GP) regression, a class of probabilistic kernel-based machine learning models, and standard logistic functions. This semi-parametric approach follows a Bayesian reasoning, in the sense of maximizing the marginal likelihood to learn the parameters and hyperparameters through a variational sparse approximation to the GP model. Using real-world operational data, the proposed approach is compared with the state-of-the-art in WTPC modeling and with an alternative probabilistic approach based on generalized linear models and logistic functions. Finally, we evaluate the proposed model in its extrapolation ability for unmodelled data. • Logistic function parametrization derived from Wind Turbine operational ranges. • Analytical procedure of parameter initialization in logistic function models. • Novel Semi-Parametric model capable of adaptation to training data and extrapolation. [ABSTRACT FROM AUTHOR]

The EU is committed to increasing the use of renewable energy sources. In the sector of transportation, the share of renewable energy is to reach 10% by 2020 and 14% by 2030, respectively, in the EU. According to the latest forecasts, the production of the first-generation biofuels made from food raw materials is showing a declining tendency in the main European producing countries. Therefore, the objective of our research is to forecast the production of some selected biofuel producing countries within the EU as well as the traditional biofuel production in Hungary. The question of land use changes due to the new regulations is crucial. Our examinations were carried out by using Verhulst's logistic function based on the biofuel production data of EUROSTAT. The function has already reached the saturation level in Germany, France and Sweden but in the case of other examined countries, biofuel production is also in the phase of slowing growth. Furthermore, findings are also justified by the 2015 regulation that restricts the share of producing first-generation biofuels in the final energy consumption to 7% and promotes the production of advanced biofuels, thereby decreasing the indirect change in land use and increasing sustainable crop production. [ABSTRACT FROM AUTHOR]

The data on Coronavirus spread in Russia (as a whole), Sweden, Moscow, and Yakutia were analyzed within the framework of kinetics of autocatalytic branching chain reactions. It was shown that in certain time intervals, the dynamics of this process can be described by a logistic function with a graph similar to the kinetic curve of product accumulation in a first-order autocatalytic reaction both in the consumption of the reactant and in the autocatalysis by the product. The logistic curve expression has a certain predictive ability and can be used to evaluate the effectiveness of anti-epidemic measures; it also poses the problem of elucidating the causes of unexpected abrupt changes in the logistic function parameters in the description of actual statistical data. [ABSTRACT FROM AUTHOR]

This paper proposes a new heavy-tailed and alternative slash type distribution on a bounded interval via a relation of a slash random variable with respect to the standard logistic function to model the real data set with skewed and high kurtosis which includes the outlier observation. Some basic statistical properties of the newly defined distribution are studied. We derive the maximum likelihood, least-square, and weighted least-square estimations of its parameters. We assess the performance of the estimators of these estimation methods by the simulation study. Moreover, an application to real data demonstrates that the proposed distribution can provide a better fit than well-known bounded distributions in the literature when the skewed data set with high kurtosis contains the outlier observations. [ABSTRACT FROM AUTHOR]

Circuit-level implementation of a novel neuron has been discussed in this article. A low-power Activation Function (AF) circuit is introduced in this paper, which is then combined with a highly linear synapse circuit to form the neu-ron architecture. Designed in Carbon Nanotube Field-Effect Transistor (CNTFET) technology, the proposed structure consumes low power, which makes it suitable for the implementation of high-throughput Neural Networks (NNs). The main advantage of the proposed AF circuit is its higher accuracy for the generation of hyperbolic tangent func-tion compared to the previously reported works. Moreover, the programmability feature for the slope and the posi-tion shifting enhances the adaptability of the designed neuron for different types of neural systems, especially Mul-ti-Layer Perceptrons (MLPs). There is also excellent compatibility between the synapse and activation circuits, which illustrates another notable privilege of the proposed neuron. Simulations using HSPICE for CNTFET 32 nm standard process have been carried out for the designed scheme to indicate the correct operation. Based on the re-sults, all of the claimed advantages can be proved clearly while the power dissipation is 6.11μW from the 0.9V pow-er supply. Also, an accuracy of 98% has been achieved for the AF circuit. [ABSTRACT FROM AUTHOR]

With the development of three-dimensional (3D) point cloud acquisition technologies, supervoxels have become increasingly important, as they provide compact and uniform representations. In this study, a novel supervoxel segmentation method, supervoxels based on local allocation (SVLA), is proposed. SVLA is composed of three steps, namely extreme point determination, local allocation (LA), and connectivity insurance. LA defines a novel cost function for preserving instance boundaries and enforces local minimization. To test the performance of SVLA, the non-compactness error (NCE) is newly defined to evaluate the compactness, and three commonly used evaluation metrics are employed. Both indoor and outdoor datasets are utilized to perform the experiments. Based on the visual and quantitative analysis of the segmentation results, SVLA demonstrates fulfillment of boundary adherence, compact constraints, and low computational complexity. Compared to state-of-the-art algorithms, SVLA yields superior results, especially with regard to indoor point clouds. [ABSTRACT FROM AUTHOR]

In this study, a new regression method called Kappa regression is introduced to model conditional probabilities. The regression function is based on Dombi's Kappa function, which is well known in fuzzy theory. Here, we discuss how the Kappa function relates to the Logistic function as well as how it can be used to approximate the Logistic function. We introduce the so-called Generalized Kappa Differential Equation and show that both the Kappa and the Logistic functions can be derived from it. Kappa regression, like binary Logistic regression, models the conditional probability of the event that a dichotomous random variable takes a particular value at a given value of an explanatory variable. This new regression method may be viewed as an alternative to binary Logistic regression, but while in binary Logistic regression the explanatory variable is defined over the entire Euclidean space, in the Kappa regression model the predictor variable is defined over a bounded subset of the Euclidean space. We will also show that asymptotic Kappa regression is Logistic regression. The advantages of this novel method are demonstrated by means of an example, and afterwards some implications are discussed. [ABSTRACT FROM AUTHOR]

Many analytical approaches to single-case data assume either linear effects (regression-based methods) or instant effects (mean-based methods). Neither assumption is realistic; therefore, these approaches' assumptions are often violated. In this article, we propose modeling curvilinear effects to appropriately parametrize the characteristics of singe-case data. Specifically, we introduce the generalized logistic function as adequate function for this situation. The merits of the proposed procedure are demonstrated using data previously used in single case research that represent typical single case data. We provide the function with auxiliary graphical options to demonstrate the model parameters. The function is freely available in the R package "userfriendlyscience." The proposed procedure is a new way to analyze single case data, which may provide applied single case researchers with a new tool to better understand their data and avoid applying methods with violated assumptions. [ABSTRACT FROM AUTHOR]

This paper develops two approximations to the normal cumulative distribution function. Although the literature is rich in approximation functions for the normal distribution, they are not very accurate. This proposed approximation is an enhanced logistic cumulative function approximation to the normal cumulative distribution function. The paper starts with approximating the logistic function to the normal distribution and then introduces a second term to the first approximation to increase the accuracy. Besides the simplicity of the introduced model, it has a maximum error of less than 0.0012 for the entire range. This level of accuracy is superior if compared to other introduced models by other authors. The current can be used to estimate the normal distribution based probabilities and associated statistics. The deviation of the proposed model from the actual normal cumulative distribution with Z score is discussed at the end of this paper. [ABSTRACT FROM AUTHOR]

The experimental paradigm of temporal reproduction has provided unique insights into the temporal machinery of cognitive processes. Both behavioral observations and electrophysiological methods with this paradigm indicate a time window of some 3 s. [ABSTRACT FROM AUTHOR]

The study uses the idea of logistic function to illustrate the presence of chaos as the cause of the absence of periodic formation. The logistic function is used in demonstrating, proving, and explaining Definition 6 and Theorem 1 through examples, tables, and figures. A system in recurrent behavior is describable when it is stable. However, chaotic behavior is seen when the system moves beyond periodic making it difficult to predict or describe the nature of the system. The WolframAlpha computational knowledge engine was used in obtaining the tables and the figures for the study. The study shows that when the parameter of the logistic function is at exactly 4 there is an uncorrelated behavior of the system indicating a new regime called chaos. Finally, the study shows that after successive iterations of the system there is no recurrent formation which is due to the system showing un-periodic, unstable, and uncorrelated. [ABSTRACT FROM AUTHOR]

This article provides a mathematical approximation model to the general double-sided truncated normal distribution, as an extension of the case of the left-sided truncated normal distribution modeled by Hamasha (2016). The right- and left-sided cases are included in the model as special cases of the introduced double-sided general model. Then, the double-sided case itself is modeled and analyzed. The double-sided truncated normal distribution is important for many applications in industrial and systems engineering. For example, in quality control, production engineers may be interested in scrapping unfit products. The distribution of product variations after this scrapping is basically a double-sided truncated distribution. A full analysis of the deviation of the introduced approximation's results from the actual results is detailed. Further, recommendations for the use of the model are suggested. The main contribution is to provide industrial practitioners with a strong tool to approximate the normal distribution when it is truncated from any or both sides for any reason. [ABSTRACT FROM AUTHOR]

A sample of fullerol C60(OH)24 was synthesized. The basic physicochemical characterization of the synthesized fullerol was done. The non-isothermal kinetics of C60(OH)24 dehydroxylation has been investigated. The thermogravimetric curves have been recorded at different heating rates ranging from 5 to 25 K min−1. By application of the Kissinger–Akahira–Sunose isoconversion method, it was found that the activation energy complexly changes with the dehydroxylation degree. The possibility of mathematical description of the kinetics of fullerol dehydroxylation by logistic function was investigated. Fullerol dehydroxylation conversion curves were completely mathematically described by the linear combination of two logistic functions at all of the investigated heating rates. The changes in the values of parameters of logistic functions with heating rate were established. It was shown that complex kinetics of C60(OH)24 dehydroxylation consists of two consecutive dehydroxylation reactions (low-temperature and high-temperature components). The values of the kinetic parameters for two components of dehydroxylation process were calculated. A model for fullerol dehydroxylation has been discussed. [ABSTRACT FROM AUTHOR]

• Subjects performed 10 kinds of cognitive tests in the three phase experiments. • The cognitive test scores had significant learning effect. • The speed of all tests and the accuracy of a few tests increased significantly. • The improvement rate of cognitive test speed was higher compared with accuracy. • The learning effect model based on logistic function had higher R2 (> 0.99). This study aimed to identify the law of cognitive test scores with the number of repetitions, and to establish a prediction model that quantitatively describes the changes in cognitive test scores. The experiments were conducted in a chamber that simulated an office environment. Participants performed neurobehavioral, Tsai-Partington, typing and d2 tests at 26 ℃. The speed and accuracy were used to reflect the cognitive test scores. The results demonstrated that a significant learning effect occurred in these cognitive test scores. The speed and accuracy of completing cognitive tests increased as the number of repetitions increased. The majority of cognitive tests could achieve stability after five repetitions and, at most, eight repetitions. The learning effect had different effects on the speed and accuracy. The percentage improvement of the speed was higher compared with accuracy, but it took more attempts to reach stability. The regression model based on the logistic function could accurately quantify the tendency for the variation in cognitive test scores with the number of repetitions. Compared with the power, linear and logarithmic functions, the logistic function had a higher R2(> 0.99). A method for correcting the learning effect of these cognitive tests was proposed based on the prediction model. [ABSTRACT FROM AUTHOR]

We aimed to derive and assess a novel metric for respiratory decline: the timing of acceleration of respiratory functional decline during the course of the disease in patients with amyotrophic lateral sclerosis (ALS). In this single-center retrospective study, we reviewed consecutive definite/probable ALS patients, diagnosed and followed up at our hospital. We recorded serial slow vital capacity (percentage of predicted slow vital capacity; %VC) since diagnosis for all patients. These serial %VC data were fitted with logistic function of the time since diagnosis, and 'acceleration point' was calculated as the week in which the second derivative of the fitted logistic function had the minimum value. We included 62 patients with ALS, whose serial %VC data had been recorded for a median of 8 times over a median of 94.3 weeks. The calculated acceleration time-point was the time-point at which the %VC is becoming 0.789 times of maximum %VC, and had a strong association with the period since diagnosis to the administration of nutritional/respiratory support (p < 0.001). Bulbar-type ALS or lower Body Mass Index at diagnosis, both are well-known ALS prognostic factors, were also associated with more rapid arrival of the acceleration time-point. We introduced the time-point of acceleration in the vital capacity decline during disease progression as a novel metric for ALS respiratory decline. Although we could not build a practically-available clinical model that directly predicts acceleration time-point due to the limited sample size, our metric may be used as one of the helpful indicators in the management during earlier disease course of ALS, such as to be careful for the potentially approaching acceleration time-point when the %VC is decreasing to approximately 0.789 times of initial %VC. • Acceleration timing of respiratory decline in ALS was introduced as a novel metric. • Logistic curve fitted well with the time-serial decline in %VC, estimating acceleration. • The acceleration timing strongly correlated with respiratory/nutritional support timing. • The acceleration time-point was at which %VC is becoming 0.789 times of maximum %VC. • This new metric may be of help in the management for earlier stage ALS. [ABSTRACT FROM AUTHOR]

The irrational emotion of the parties concerned can cause the deviation of decision-making and behavior during the emergency management of unexpected mass incidents. Many experiments show that when the occurrence probability is lower, player is inclined to the pessimistic emotion. However, when the occurrence probability becomes very higher, player begins to turn the optimistic emotion. Considering the above logistic emotional utility of both players, we build a simultaneous-move game model of unexpected mass incidents with the logistic emotional utility, where the two players are the dominant group and the vulnerable group, respectively. Lastly, we make the numerical analysis based on the case of Dalian PX incident. The results show that when only the social strong group has logistic emotion, its pessimistic emotion leads to the occurrence of mass incidents. The unilateral logistic emotion of the vulnerable group is conducive to the reconciliation of the situation, especially its optimistic emotion makes the situation reach a harmonious bilateral cooperation. However, both sides having obvious emotional fluctuation aggrade the uncertainty of the situation. Therefore, local government strengthens information collection, situation assessment and emergency drills increasing the handling experience prompting the situation to converge to a stable equilibrium. [ABSTRACT FROM AUTHOR]

Recently, it has been observed that topological ultrametricity of data can be expressed as an integral over a function which describes local ultrametricity. It was then observed empirically that this function begins as a sharply decreasing function, in order to increase again back to one. After providing a method for estimating the falling part of the local ultrametricity of data, empirical evidence is given for its logistic behaviour in relation to the number of connected components of the Vietoris-Rips graphs involved. The result is a functional dependence between that number and the number of maximal cliques. Further, it turns out that the logistic parameters depend linearly on the datasize. These observations are interpreted in terms of the Erdős-Rényi model for random graphs. Thus the findings allow to define a percolationbased index for almost ultrametricity which can be estimated in O(N2 logN) time which is more efficient than most ultrametricity indices. [ABSTRACT FROM AUTHOR]

Abstract Modeling of wind turbine power curve which shows the relationship between wind speed and its power output can be used as an important tool in monitoring and forecasting wind energy. A data-driven approach to find most probable probability distribution function (PDF) for wind speed and turbine power is presented in this study. Equations for the scale and shape parameters in the Weibull wind speed distribution and equations for the four parameters in the logistic function were obtained explicitly by maximum likelihood estimation (MLE) method. With help of a selected data set from the wind speed and the corresponding power output data which was collected over a period of a year, the values of the parameters were obtained by solving the equations by iteration procedures. The predicted powers by the obtained logistic function closely follow the measured turbine powers averaged at 5-min or 10-min. Monitoring turbine power output by the logistic function was also tested for the measured powers in other time duration. Highlights • Modeling of power curve for wind turbine was constructed by data-driven approach. • Equations for the four parameters of a logistic function were obtained by maximum likelihood estimation (MLE) method. • Most probable probability distribution function (PDF) for wind turbine power was obtained by solving the equations. • The predicted power by logistic function closely follows the measured power averaged at 5-min or 10-min. [ABSTRACT FROM AUTHOR]

In this paper, the design procedure of a novel neuron is discussed. Starting from the activation function circuit which is based on the modification of regulated cascode structure, a new scheme is presented which can produce the logistic function with high percentage of accuracy. The main advantage of the proposed function generator circuit is its full programmability feature in which its output waveform slope can easily be adjusted by means of the control voltages applied to the bulks of input stage metal oxide semiconductor (MOS) transistors. Also, the output waveform can easily be converted to a step function with the help of bias changes for the input transistors. Following the concepts of the previous work by the authors, the designed activation function has a good compatibility with the employed synapse. Low power and small active-area consumption are the other features of the proposed circuit which qualify it for hardware implementation of neural networks. Post-layout simulation results based on complementary metal-oxide-semiconductor (CMOS) 0.18 µm twin-tub fabrication standard process depict the correct behaviour of the designed circuits and demonstrate 96% accuracy for the proposed activation function structure. The power dissipation of the activation function circuit is 72 µW at the worst case for 1.8 power supply voltage. [ABSTRACT FROM AUTHOR]