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

MicroRNAs (miRNAs) exert an enormous influence on cell differentiation, biological development and the onset of diseases. Because predicting potential miRNA-disease associations (MDAs) by biological experiments usually requires considerable time and money, a growing number of researchers are working on developing computational methods to predict MDAs. High accuracy is critical for prediction. To date, many algorithms have been proposed to infer novel MDAs. However, they may still have some drawbacks. In this paper, a logistic weighted profile-based bi-random walk method (LWBRW) is designed to infer potential MDAs based on known MDAs. In this method, three networks (i.e., a miRNA functional similarity network, a disease semantic similarity network and a known MDA network) are constructed first. In the process of building the miRNA network and the disease network, Gaussian interaction profile (GIP) kernel is computed to increase the kernel similarities, and the logistic function is used to extract valuable information and protect known MDAs. Next, the known MDA matrix is preprocessed by the weighted K-nearest known neighbours (WKNKN) method to reduce the number of false negatives. Then, the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network. Finally, the predictive ability of the LWBRW method is confirmed by the average AUC of 0.939 3 (0.006 1) in 5-fold cross-validation (CV) and the AUC value of 0.976 3 in leave-one-out cross-validation (LOOCV). In addition, case studies also show the outstanding ability of the LWBRW method to explore potential MDAs. [ABSTRACT FROM AUTHOR]

Featured Application: The research proposed in this paper could be useful in wind power forecasting, condition monitoring and energy assessment of wind turbine. The wind turbine power curve (WTPC) is of great significance for wind power forecasting, condition monitoring, and energy assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Firstly, we combine the asymmetric absolute value function from the quantile regression (QR) cost function with logistic functions (LF), so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles without considering the prior distribution of wind power. Among them, three optimization algorithms are selected to make comparative studies. Secondly, an adaptive outlier filtering method is developed based on QRLF, which can eliminate the outliers by the symmetrical relationship of power distribution. Lastly, supervisory control and data acquisition (SCADA) data collected from wind turbines in three wind farms are used to evaluate the performance of the proposed method. Five evaluation metrics are applied for the comparative analysis. Compared with typical WTPC models, QRLF has better fitting performance in both deterministic and probabilistic power curve modeling. [ABSTRACT FROM AUTHOR]

In this study, a laboratory experiment has been carried out to study the effect of a static magnetic field (SMF) of 125 mT on barley seeds. For evaluation of these experiments, some germination parameters have been recorded. Also, two mathematical models, Logistic (Log) and Gompertz (Gom) functions were performed in each treatment, for comparative analyses of parameters of the cumulative germination curves and mean square error (MSE). The best values have been found at treatment MG3 in FGP (95%), S.G (5.66 seed day-1), MGT (3.61 days), GRI (28.31% day-1), and GI (84). The highest value of asymptotic germination barley seeds was recorded in the Gom equation (96.78%) at the exposure time of 6 h (MG3). As well as, the best maximum germination rates reached 2.07% h-1 with Gom functions at MG2, while the highest value of the germination percentage of the inflection point has been reported in the Log function for 47.21 at MG3. For the fit growth curve, the results have demonstrated that the Log function has given the lowest values in MSE in all MG exposure times compared with the Gom function. So, the Log function has shown a more fitting in all MG treatments, as compared with the Gom function. [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]

Loop-mediated isothermal amplification (LAMP) is an exponential amplification method of DNA strands that is more and more used for its high performances. Thanks to its high sensitivity and selectivity, LAMP found numerous applications from the detection of pathogens or viruses through their genome amplification to its incorporation as an amplification strategy in protein or miRNA biomarker quantification. The LAMP method is composed of two stages: the first one consists in the transformation of the DNA strands into dumbbell structures formed of two stems and loops thanks to four primers; then, in the second stage, only two primers are required to amplify the dumbbells exponentially in numerous hairpins of increasing lengths. In this paper, we propose a theoretical framework to analyze the kinetics of the second stage of LAMP, the isothermal dumbbell exponential amplification (IDEA) as function of the physico-chemical parameters of the amplification reaction. Dedicated experiments validate the models. We believe these results may help the optimization of LAMP performances by reducing the number of experiments necessary to find the best parameters.

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]

Coronavirus disease 2019 outbreak has spread as a pandemic since the end of year 2019. This situation has been causing a lot of problems of human beings such as economic problems, health problems. The forecasting of the number of infectious people is required by the authorities of all countries including Southeast Asian countries to make a decision and control the outbreak. This research is to investigate the suitable forecasting model for the number of infectious people in Southeast Asian countries. A comparison of forecasting models between logistic growth curve which is symmetric and Gompertz growth curve which is asymmetric based on the maximumof Coefficient of Determination and theminimumof RootMean Squared Percentage Error is also proposed. The estimation of parameters of the forecasting models is evaluated by the least square method. In addition, spreading of the outbreak is estimated by the derivative of the number of cumulative cases. The findings show that Gompertz growth curve is a suitable forecasting model for Indonesia, Philippines, andMalaysia and logistic growth curve suits the other countries in South Asia. [ABSTRACT FROM AUTHOR]

Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation.In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).We found strong agreement between SOS estimates from logistic and spline models (rEVI = 0.86; rNDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSDEVI = 3.07, RMSDNDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (rEVI = 0.51, rNDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series.

Two separated sets of laboratory experiments were studied for barley seeds treating using a microwave and ultraviolet irradiation. In the microwave set, seeds have been exposed to the microwave radiations (2450 MHz) for 0 sec (control, MW0), 5 sec (MW1), 10 sec (MW2), and 20 sec (MW3), while in the ultraviolet set, seeds have exposed to UV-C radiation (254 nm) for 0 min (control, UV0), 30 min (UV1), 60 min (UV2), and 120 min (UV3). The aim is to study the influences of different exposure time from MW and UV-C radiation on some barley seed germination parameters and to choose the fitting model Logistic (Log) or Gompertz (Gom) suited to cumulative germination curves under the influence of these factors. The results of this study showed higher seed germination percentage (93.33%) at the exposure time MW2 and UV3 (88.33%), whereas the lowest value (66.67%) recorded in MW3 treatment. The results also appeared the best values at MW2 in SG, 6.24 seed day-1; in GRI, 31.19% day-1, and in GI, 87.67, as well as at UV2 in MGT, 3.32 day. The higher value of asymptotic germination barley seeds was found with Gom function (97.24%, and 88.71%) at MW2 and UV3, respectively. Besides, Gom functions at MW1 and UV2 give the highest maximum germination rates at 2,08 and 2.51% h-1, respectively. The results of the Log equation illustrated the highest value of germination percentage of the inflection point has recorded in 43.85 and 47.37 % on UV3 and MW2 treatments, respectively. For the fitting growth curve, the results have proven that the Gom function was shown the lowest values in MSE in all MW and UV exposure times, as compared with the Log function. So, the results of the Gom function were more fit for the growth curve for MW and UV treatments, as compared with the Log function.

A viscosity model for shear thickening fluids (STFs) based on phenomenological theory is proposed. The model considers three characteristic regions of the typical material properties of STFs: a shear thinning region at low shear rates, followed by a sharp increase in viscosity above the critical shear rate, and subsequently a significant failure region at high shear rates. The typical S-shaped characteristic of the STF viscosity curve is represented using the logistic function, and suitable constraints are applied to satisfy the continuity of the viscosity model. Then, the Levenberg–Marquardt algorithm is introduced to fit the constitutive model parameters based on experimental data. Verification against experimental data shows that the model can predict the viscosity behavior of STF systems composed of different materials with different mass concentrations and temperatures. The proposed viscosity model provides a calculation basis for the engineering applications of STFs (e.g., in increasing impact resistance and reducing vibration).

In the study it was attempted to describe the growth of oriental goat’s rue (Galega orientalis Lam.) by means of a logistic function, Gompertz function and Richards function. Analysis of data was based on measurements of stem lengths of oriental goat’s rue (Galega orientalis Lam.) cv. Gale which were taken during a four-year period as part of a long-term experiment set up in 2009 at the experimental site of the University of Natural Sciences and Humanities. From 2009 to 2012, 70 plants were randomly selected every seven days from the start of growth to the stage of full maturity of pods. The beginning of growth was assumed to be the moment of forming leaf buds on root necks of the plants which overwintered. The study demonstrated that the functions described the empirical data well as the coefficients of determination obtained ranged from 9 to 99%, depending on the growing season. The estimated function parameters indicate that both plant height and growth rate were affected by thermal conditions and moisture during the growing season. The maximum growth rate ranged from 2.18 to 4.04 cm per day. Also the inflection point, the point at which plants grew the fastest, depended on the conditions of the growing season.

The paper deals with a new analytical model of the classical load of a heat supply system. The proposed model offers better quality approximation of actual consumption curves. The model includes two components -a power function (the Sochinsky-Rossander model) and a modified logistics function for which their integrals are, in essence, traced. Determination of TMtime allows to calculate the load factor and the annual energy consumption.

Detection of source horizontal boundaries is a common feature in the interpretation of magnetic and gravity data. A wide range of derivative- and phase-based methods are available to solve this problem. Here, we compare the effectiveness of the commonly used methods, and introduce a method based on the logistic function and the horizontal gradient amplitude, which shows improved performance as a boundary detection filter. The effectiveness of the proposed filter is demonstrated by evaluating synthetic examples and a real example from the Central Puget Lowland (United States). The main advantage of this method is that it provides high-resolution results, and can avoid producing spurious boundaries in the output maps. [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]

Copyright of Asian Journals of Ecotoxicology is the property of Gai Kan Bian Wei Hui 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.)

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]

Power curves provided by wind turbine manufacturers are obtained under certain conditions that are different from those of real life operation and, therefore, they actually do not describe the behavior of these machines in wind farms. In those cases where one year of data is available, a logistic function may be fitted and used as an accurate model for such curves, with the advantage that it describes the power curve by means of a very simple mathematical expression. Building such a curve from data can be achieved by different methods, such as using mean values or, alternatively, all the possible values for given intervals. However, when using the mean values, some information is missing and when using all the values the model obtained can be wrong. In this paper, some methods are proposed and applied to real data for comparison purposes. Among them, the one that combines data clustering and simulation is recommended in order to avoid some errors made by the other methods. Besides, a data filtering recommendation and two different assessment procedures for the error provided by the model are proposed. [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]

A three-part series presents the development of models for predicting the local thermal sensation (Part I) and local comfort (Part II) of different parts of the human body, and also the whole-body sensation and comfort responses (Part III). The models predict these subjective responses to the environment from thermophysiological measurements or predictions (skin and core temperatures). The models apply to a range of environments: uniform and non-uniform, transient and stable. They are based on diverse results from the literature and from body-part-specific human subject tests in a climate chamber. They were validated against a test of passengers in automobiles. This series is intended to present the rationale, structure, and coefficients for these models so that others can test and develop them further as additional empirical data becomes available. The experimental methods and some measured results from the climate chamber tests have been published previously. Part I describes thermal sensation models representing 19 individual local body parts. The models’ structure and coefficients were derived by regression of skin and core temperatures against thermal sensation votes obtained in the chamber experiments. The sensation for each local body part is predicted by a logistic function with four inputs: local skin temperature, mean skin temperature presenting the whole body thermal state, and the time derivatives of skin and core temperatures representing the response to transients. These inputs can be obtained from thermophysiological computer programs that treat the body as multiple segments.

Most models reported in the current process target literature assume a nominal-the-best type quality characteristic in determining the optimal process mean value. In real-world industrial settings, however, a number of quality characteristics of interest are of a larger-the-better type ( L -type), and the problem of jointly determining the optimum process mean and tolerance limit for the L -type quality characteristic may be of significant practical importance, yet this has largely been overlooked in the literature. In this paper, cost-effective optimization models are developed, and the methods of finding optimum solutions are presented. Numerical examples and sensitivity analysis are given. [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.

In the present paper, we model the cumulative number of persons, reported to be infected with COVID-19 virus, by a sum of several logistic functions (the so-called multilogistic function). We introduce logistic wavelets and describe their properties in terms of Eulerian numbers. Moreover, we implement the logistic wavelets into Matlab’s Wavelet Toolbox and then we use the continuous wavelet transform (CWT) to estimate the parameters of the approximating multilogistic function. Using the examples of several countries, we show that this method is effective as a method of fitting a curve to existing data. However, it also has a predictive value, and, in particular, allows for an early assessment of the size of the emerging new wave of the epidemic, thus it can be used as an early warning method.

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]

We introduce a family of nonlinear transformations of the traditional cosine curve used in the modeling of biological rhythms. The nonlinear transformation is the sigmoidal family, represented here by three family members: the Hill function, the logistic function, and the arctangent function. These transforms add two additional parameters that must be estimated, in addition to the acrophase, MESOR, and amplitude (and period in some applications), but the estimated curves have shapes requiring many more than 2 additional harmonics to achive the same fit when modeled by harmonic regression. Particular values of the additional parameters can yield rectangular waves, narrow pulses, wide pulses, and for rectangular waves (representing alternating “on” and “off” states) the times of onset and offset. We illustrate the sigmoidally transformed cosine curves, and compare them to harmonic regression modeling, in a sample of 8 activity recordings made on patients in a nursing home. We introduce a family of nonlinear transformations of the traditional cosine curve used in the modeling of biological rhythms. The nonlinear transformation is the sigmoidal family, represented here by three family members: the Hill function, the logistic function, and the arctangent function. These transforms add two additional parameters that must be estimated, in addition to the acrophase, MESOR, and amplitude (and period in some applications), but the estimated curves have shapes requiring many more than 2 additional harmonics to achive the same fit when modeled by harmonic regression. Particular values of the additional parameters can yield rectangular waves, narrow pulses, wide pulses, and for rectangular waves (representing alternating “on” and “off” states) the times of onset and offset. We illustrate the sigmoidally transformed cosine curves, and compare them to harmonic regression modeling, in a sample of 8 activity recordings made on patients in a nursing home.

Because an outgrowth of auxiliary bud in plant is regulated by light quality detected by phytochrome, branching differences in various environment or cultivars in soybean would be the results of the responses to light environment. Therefore, we analyzed differences in the number of branches of two determinate soybean cultivars, ‘Hatsusayaka’ and ‘Sachiyutaka’, between low and high planting densities in relation to light quality within the canopy. We compared changes in the ratio of red to far red (R:FR) irradiance at the ground level over time and with canopy height with those in the fraction of intercepted photosynthetically active radiation (FIPAR) and leaf area index (LAI). Regardless of time or distance from the top of the canopy, the changes in R:FR were sigmoidal, and were symmetrical with those of FIPAR and LAI. The effects of density and cultivar on FIPAR, LAI, and R:FR could be modeled with a logistic function. The number of branches was greater at low density than at high density, and in Hatsusayaka than in Sachiyutaka. However, there were no notable differences in the dynamics of R:FR between planting densities or between cultivars. Close relationships between parameters of the dynamics of the changes in R:FR and of those in FIPAR and LAI suggest that FIPAR and LAI are major factors that regulate R:FR, regardless of time or canopy depth. We found no evidence of a causal relationship between the dynamics of R:FR within the canopy over time or with canopy depth and number of branches of either cultivar.

It was attempted in the paper to describe the growth of oriental goat’s rue (Galega orientalis Lam.) growth by means of a logistic function. The studies demonstrated that the function describes empirical data well as the coefficients of determination obtained ranged between 97 to 98%, depending on the growing season. The estimated function parameters indicate that both the plant height and growth rate were conditioned by thermal conditions and moisture during the growing season. Plants had the longest stems in the year 2010 (127.9 cm), and the shortest in 2008 (105.9 cm). The maximum growth rate ranged from 2.12 to 3.97 cm per day in, respectively, 2009 and 2008. Also the inflection point, the point at which plants grew the fastest, depended on the conditions of the growing season.

Theoretical principles of sustainable aquifer management are laid out in this work. The premise of our treatment is that groundwater is a renewable, although exhaustible, natural resource. The theory of this work is aimed at aquifers with a relatively homogeneous recharge that can be approximated by a logistic growth function. Sustainable aquifer exploitation occurs when the rate of ground-water extraction is equal to or less than the natural rate of groundwater replenishment for any level of aquifer storage. There can be many levels of sustainable aquifer exploitation depending on the level of aquifer storage, but there may be only one that maximizes economic returns under a variety of economic and aquifer conditions. Different strategies for sustainable exploitation are derived depending on whether or not the analysis considers tradeoffs among: (i) current and future exploitation; (ii) constant and dynamic aquifer storage conditions; and (iii) regulated and unregulated aquifer exploitation. Key factors affecting sustainable exploitation strategies include: (1) the market price of groundwater; (2) the cost of groundwater extraction; (3) the aquifer storage and natural replenishment characteristics; (4) institutional and environmental regulations on groundwater extraction; and (5) the real discount rate. An example of sustainable groundwater exploitation in Santa Barbara, California, illustrates the methods of this article. © 2001, Taylor & Francis Group, LLC.

The wind turbine power curve (WTPC) is of great significance for wind power forecasting, condition monitoring, and energy assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Firstly, we combine the asymmetric absolute value function from the quantile regression (QR) cost function with logistic functions (LF), so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles without considering the prior distribution of wind power. Among them, three optimization algorithms are selected to make comparative studies. Secondly, an adaptive outlier filtering method is developed based on QRLF, which can eliminate the outliers by the symmetrical relationship of power distribution. Lastly, supervisory control and data acquisition (SCADA) data collected from wind turbines in three wind farms are used to evaluate the performance of the proposed method. Five evaluation metrics are applied for the comparative analysis. Compared with typical WTPC models, QRLF has better fitting performance in both deterministic and probabilistic power curve modeling.

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]

Dispatching unmanned aerial vehicles (UAVs) to harvest sensing-data from distributed sensors is expected to significantly improve the data collection efficiency in conventional wireless sensor networks (WSNs). In this paper, we consider a UAV-enabled WSN, where a flying UAV is employed to collect data from multiple sensor nodes (SNs). Our objective is to maximize the minimum average data collection rate from all SNs subject to a prescribed reliability constraint for each SN by jointly optimizing the UAV communication scheduling and three-dimensional (3D) trajectory. Different from the existing works that assume the simplified line-of-sight (LoS) UAV-ground channels, we consider the more practically accurate angle-dependent Rician fading channels between the UAV and SNs with the Rician factors determined by the corresponding UAV-SN elevation angles. However, the formulated optimization problem is intractable due to the lack of a closed-form expression for a key parameter termed effective fading power that characterizes the achievable rate given the reliability requirement in terms of outage probability. To tackle this difficulty, we first approximate the parameter by a logistic (“S” shape) function with respect to the 3D UAV trajectory by using the data regression method. Then, the original problem is reformulated to an approximate form, which, however, is still challenging to solve due to its non-convexity. As such, we further propose an efficient algorithm to derive its suboptimal solution by using the block coordinate descent technique, which iteratively optimizes the communication scheduling, the UAV’s horizontal trajectory, and its vertical trajectory. The latter two subproblems are shown to be non-convex, while locally optimal solutions are obtained for them by using the successive convex approximation technique. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithm and draw new insights on the 3D UAV trajectory under the Rician fading as compared to conventional LoS channel models. [ABSTRACT FROM AUTHOR]

This article presents a comparative study of empirical power curve models to estimate the output power of the turbine as a function of the wind speed. In these models, modelling strategy relies on the objective of modelling, data being used for the modelling and targeted accuracy. It has been observed that models based on presumed shape of power curve lack desired accuracy since these are developed using the power ratings of wind turbine which are not sufficient to exactly replicate the turbine's actual behaviour. The performance of various models which comes under manufacturer power curve modelling methodology has been compared with reference to commercially available wind turbines. It has been found that power curves obtained through method of least squares and cubic spline interpolation methods exactly match with manufacturer power curve, whereas 5PL method gives sufficiently accurate results. Modelling based on actual data of wind farm has been found to be a powerful technique for developing site-specific power curves. [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]

The path-following (PF) problem of an autonomous underwater vehicle (AUV) is studied, in which the path convergence is viewed as the main task while the speed profile is also taken into consideration as a secondary task. To accommodate the prioritized PF tasks, a novel multiobjective model predictive control (MPC) (MOMPC) framework is developed. Two methods, namely, weighted sum (WS) and lexicographic ordering, are investigated for solving the MOMPC PF problem. A logistic function is proposed for the WS method in an attempt to automatically select the appropriate weights. The Pontryagin minimum principle is subsequently applied for the WS-MOMPC implementation. The implicit relation between the two methods is shown, and the convergence of the solution with the MOMPC PF control algorithms is analyzed. Simulation studies on the Saab SeaEye Falcon AUV demonstrate the effectiveness of the proposed MOMPC PF control. [ABSTRACT FROM AUTHOR]