ON THE APPROXIMATION OF THE GENERALIZED CUT FUNCTION OF DEGREE p+1 BY SMOOTH HYPER–LOG–LOGISTIC FUNCTION

This chapter presents an introductory overview of concepts that underscore the general framework of item response theory. “Item response theory” is a broad umbrella term used to describe a family of mathematical measurement models that consider observed test scores to be a function of latent, unobservable constructs. Most musical constructs cannot be directly measured and are therefore unobservable. Musical constructs can therefore only be inferred based on secondary, observable behaviors. Item response theory uses observable behaviors as probabilistic distributions of responses as a logistic function of person and item parameters in order to define latent constructs. This chapter describes philosophical, theoretical, and applied perspectives of item response theory in the context of measuring musical behaviors.

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Effects of habitat edges on vegetation structure and the vulnerable golden-brown mouse lemur (Microcebus ravelobensis) in northwestern Madagascar

BMC Ecology ◽

10.1186/s12898-020-00337-z ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Bertrand Andriatsitohaina ◽

Daniel Romero-Mujalli ◽

Malcolm S. Ramsay ◽

Frederik Kiene ◽

Solofonirina Rasoloharijaona ◽

...

Keyword(s):

Body Mass ◽

Edge Effects ◽

Vegetation Structure ◽

Species Abundance ◽

Tree Height ◽

Logistic Function ◽

Study Region ◽

Large Trees ◽

Significant Difference ◽

Vegetation Parameters

Abstract Background Edge effects can influence species composition and community structure as a result of changes in microenvironment and edaphic variables. We investigated effects of habitat edges on vegetation structure, abundance and body mass of one vulnerable Microcebus species in northwestern Madagascar. We trapped mouse lemurs along four 1000-m transects (total of 2424 trap nights) that ran perpendicular to the forest edge. We installed 16 pairs of 20 m2 vegetation plots along each transect and measured nine vegetation parameters. To determine the responses of the vegetation and animals to an increasing distance to the edge, we tested the fit of four alternative mathematical functions (linear, power, logistic and unimodal) to the data and derived the depth of edge influence (DEI) for all parameters. Results Logistic and unimodal functions best explained edge responses of vegetation parameters, and the logistic function performed best for abundance and body mass of M. ravelobensis. The DEI varied between 50 m (no. of seedlings, no. of liana, dbh of large trees [dbh ≥ 10 cm]) and 460 m (tree height of large trees) for the vegetation parameters, whereas it was 340 m for M. ravelobensis abundance and 390 m for body mass, corresponding best to the DEI of small tree [dbh < 10 cm] density (360 m). Small trees were significantly taller and the density of seedlings was higher in the interior than in the edge habitat. However, there was no significant difference in M. ravelobensis abundance and body mass between interior and edge habitats, suggesting that M. ravelobensis did not show a strong edge response in the study region. Finally, regression analyses revealed three negative (species abundance and three vegetation parameters) and two positive relationships (body mass and two vegetation parameters), suggesting an impact of vegetation structure on M. ravelobensis which may be partly independent of edge effects. Conclusions A comparison of our results with previous findings reveals that edge effects are variable in space in a small nocturnal primate from Madagascar. Such an ecological plasticity could be extremely relevant for mitigating species responses to habitat loss and anthropogenic disturbances.

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Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function

Energies ◽

10.3390/en14041091 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1091

Author(s):

Liulei Bao ◽

Guangcheng Zhang ◽

Xinli Hu ◽

Shuangshuang Wu ◽

Xiangdong Liu

Keyword(s):

Direct Method ◽

Logistic Function ◽

New Method ◽

Threshold Function ◽

Critical Threshold ◽

Time Curve ◽

Acceleration Rate ◽

Deformation Stages ◽

Distance Weighting ◽

Landslide Deformation

The cumulative displacement-time curve is the most common and direct method used to predict the deformation trends of landslides and divide the deformation stages. A new method based on the inverse logistic function considering inverse distance weighting (IDW) is proposed to predict the displacement of landslides, and the quantitative standards of dividing the deformation stages and determining the critical sliding time are put forward. The proposed method is applied in some landslide cases according to the displacement monitoring data and shows that the new method is effective. Moreover, long-term displacement predictions are applied in two landslides. Finally, summarized with the application in other landslide cases, the value of displacement acceleration, 0.9 mm/day2, is suggested as the first early warning standard of sliding, and the fitting function of the acceleration rate with the volume or length of landslide can be considered the secondary critical threshold function of landslide failure.

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Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

Information ◽

10.3390/info12030109 ◽

2021 ◽

Vol 12 (3) ◽

pp. 109 ◽

Cited By ~ 1

Author(s):

Iman Rahimi ◽

Amir H. Gandomi ◽

Panagiotis G. Asteris ◽

Fang Chen

Keyword(s):

Machine Learning ◽

Logistic Function ◽

Prediction Performance ◽

Machine Learning Algorithms ◽

Model Parameters ◽

The Novel ◽

Chinese City ◽

Limited Memory ◽

Increasing Trend ◽

Novel Coronavirus

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

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Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

Water ◽

10.3390/w13050705 ◽

2021 ◽

Vol 13 (5) ◽

pp. 705

Author(s):

Josué Trejo-Alonso ◽

Carlos Fuentes ◽

Carlos Chávez ◽

Antonio Quevedo ◽

Alfonso Gutierrez-Lopez ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Hydraulic Conductivity ◽

Input Data ◽

Logistic Function ◽

Saturated Hydraulic Conductivity ◽

Irrigation District ◽

Pedotransfer Functions ◽

Back Propagation Algorithm ◽

Artificial Neural

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (KS) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R2, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results.

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A Note on the Estimation of the Parameters of the Logistic Function, Using the Minimum Logit χ 2 Method

Biometrika ◽

10.2307/2333488 ◽

1962 ◽

Vol 49 (1/2) ◽

pp. 250 ◽

Cited By ~ 1

Author(s):

Shirley E. Hitchcock

Keyword(s):

Logistic Function

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Selective impairment of central mediation of baroreflex in anesthetized young adult Fischer 344 rats after chronic intermittent hypoxia

AJP Heart and Circulatory Physiology ◽

10.1152/ajpheart.00358.2007 ◽

2007 ◽

Vol 293 (5) ◽

pp. H2809-H2818 ◽

Cited By ~ 50

Author(s):

He Gu ◽

Min Lin ◽

Jianyu Liu ◽

David Gozal ◽

Karie E. Scrogin ◽

...

Keyword(s):

Intermittent Hypoxia ◽

Maximum Amplitude ◽

Logistic Function ◽

Stimulus Onset ◽

Chronic Intermittent Hypoxia ◽

Baroreflex Control ◽

Maximum Slope ◽

Peak Response ◽

Fischer 344 ◽

Arterial Blood

Baroreflex control of heart rate (HR) is impaired after chronic intermittent hypoxia (CIH). However, the location and nature of this response remain unclear. We examined baroreceptor afferent, vagal efferent, and central components of the baroreflex circuitry. Fischer 344 (F344) rats were exposed to room air (RA) or CIH for 35–50 days and were then anesthetized with isoflurane, ventilated, and catheterized for measurement of mean arterial blood pressure (MAP) and HR. Baroreceptor function was characterized by measuring percent changes of integrated aortic depressor nerve (ADN) activity (Int ADNA) relative to the baseline value in response to sodium nitroprusside- and phenylephrine-induced changes in MAP. Data were fitted to a sigmoid logistic function curve. HR responses to electrical stimulation of the left ADN and the right vagus nerve were assessed under ketamine-acepromazine anesthesia. Compared with RA controls, CIH significantly increased maximum baroreceptor gain or maximum slope, maximum Int ADNA, and Int ADNA range (maximum − minimum Int ADNA), whereas other parameters of the logistic function were unchanged. In addition, CIH increased the maximum amplitude of bradycardic response to vagal efferent stimulation and decreased the time from stimulus onset to peak response. In contrast, CIH significantly reduced the maximum amplitude of bradycardic response to left ADN stimulation and increased the time from stimulus onset to peak response. Therefore, CIH decreased central mediation of the baroreflex but augmented baroreceptor afferent function and vagal efferent control of HR.

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Tests of Hypotheses About the Parameters of the Logistic Function

Biometrika ◽

10.2307/2333657 ◽

1966 ◽

Vol 53 (3/4) ◽

pp. 535

Author(s):

Shirley E. Hitchcock

Keyword(s):

Logistic Function ◽

Tests Of Hypotheses

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Nonsubmodular Constrained Profit Maximization in Attribute Networks

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595922400061 ◽

2021 ◽

Author(s):

Liman Du ◽

Wenguo Yang ◽

Suixiang Gao

Keyword(s):

Profit Maximization ◽

Logistic Function ◽

Influence Maximization ◽

Maximization Problem ◽

Cardinality Constraint ◽

Three Phase ◽

Marginal Increment ◽

The Difference ◽

Classical Influence ◽

Influence Maximization Problem

The number of social individuals who interact with their friends through social networks is increasing, leading to an undeniable fact that word-of-mouth marketing has become one of the useful ways to promote sale of products. The Constrained Profit Maximization in Attribute network (CPMA) problem, as an extension of the classical influence maximization problem, is the main focus of this paper. We propose the profit maximization in attribute network problem under a cardinality constraint which is closer to the actual situation. The profit spread metric of CPMA calculates the total benefit and cost generated by all the active nodes. Different from the classical Influence Maximization problem, the influence strength should be recalculated according to the emotional tendency and classification label of nodes in attribute networks. The profit spread metric is no longer monotone and submodular in general. Given that the profit spread metric can be expressed as the difference between two submodular functions and admits a DS decomposition, a three-phase algorithm named as Marginal increment and Community-based Prune and Search(MCPS) Algorithm frame is proposed which is based on Louvain algorithm and logistic function. Due to the method of marginal increment, MPCS algorithm can compute profit spread more directly and accurately. Experiments demonstrate the effectiveness of MCPS algorithm.

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