Effect of Undecided and Swing Voters on The Dynamics Voters Model in Presidential Elections

In this paper, we construct the NUS1S2A voters model of two political fanaticism figures which involves undecided and swing voters. We determine the equilibrium points and the threshold parameter of the voters model. We also perform a sensitivity analysis for the threshold number to determine the importance of model parameters. The results of the sensitivity analysis show that the rate of transfer from neutral voters to undecided and swing voters is not the most negative sensitive parameter of the model, even though an increase in its parameter will cause a decrease in voter interest in voting in the presidential elections.

Segmentasi Citra Wajah dengan Implementasi Adaptif Threshold- Integral Image

<p>Segmentasi wajah merupakan bagian penting dalam pengolahan citra digital untuk mengetahui objek wajah dalam citra sebelum dilakukan pendeteksian ekspresi wajah. Adaptif <em>Threshold – Integral Image</em> adalah salah satu teknik segmentasi berbasis <em>pixel-based</em>,<em> </em>yaitu <em>local thresholding</em>. Penelitian ini bertujuan untuk memisahkan objek wajah manusia dan <em>background </em>-nya. Citra wajah yang akan digunakan nanti citra di dalam ruangan (<em>indoor</em>)<em> </em>dan di luar ruangan (<em>outdoor</em>) dengan resolusi gambar 300x400 piksel. Pada penelitian ini juga mencari nilai parameter S (<em>kernel</em>) dan T (<em>threshold</em>) yang terbaik dengan melakukan 16 kali percobaan. Dan didapatkan hasil terbaik, yaitu citra di dalam ruangan (<em>indoor</em>) nilai S=1/2 dan T=50, serta citra di luar ruangan (<em>outdoor</em>) nilai S=1/30 dan T=30. Segmentasi citra wajah dengan menggunakan metode Adaptif <em>Threshold – Integral Image</em> <em>robust</em> (kuat) terhadap intensitas cahaya tinggi dan rendah dengan mengatur nilai parameter S (<em>kernel</em>) dan T (<em>Threshold</em>) maka metode ini mampu memisahkan objek wajah dan <em>background</em> -nya. Dari hasil uji coba <em>threshold</em> menggunakan metode Adaptif <em>Threshold – Integral Image</em> terhadap citra di dalam ruangan (<em>indoor)</em> dan di luar ruangan (<em>outdoor)</em> menghasilkan <em>thresholding</em> yang baik dengan mempertimbangkan nilai parameter S (<em>kernel</em>) dan T (<em>threshold</em>) memberikan hasil dengan tingkat akurasi yang tinggi, yaitu citra di dalam ruangan (<em>indoor</em>) sebesar 96.72%, dan citra di luar ruangan (<em>outdoor</em>) sebesar 93.59%.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Face segmentation is an important in digital image processing to find out the object's face in the image before detecting facial expressions. Adaptive Threshold - Integral Image is a pixel-based segmentation technique, which is local thresholding. This study is intended to split the object of a human face and its background. Face images that will be used later in indoor and outdoor with an image resolution of 300x400 pixels. This study also searched for the best S (kernel) and T (threshold) parameter values by performing 16 experiments. And the best results are obtained, name the image in the room (indoor) the value of S = 1/2 and T = 50, and the image outside the room (outdoor) the value of S = 1/30 and T = 30. Face image segmentation using the Adaptive Threshold - Integral Image robust method of high and low light intensity by setting the S (kernel) and T (Threshold) parameter values, this method is able to split the face object and its background. From the results of the threshold trial using the Adaptive Threshold - Integral Image method for indoor and outdoor images produces a good thresholding by considering the values of the S (kernel) and T (threshold) parameters to give results with a high degree of accuracy, that is indoor images of 96.72%, and outdoor images of 93.59%.<strong></strong></em></p><p><em><strong><br /></strong></em></p>

Factor-augmented forecasting regressions with threshold effects

This paper introduces a factor-augmented forecasting regression model in the presence of threshold effects. We consider least squares estimation of the regression parameters, and establish asymptotic theories for estimators of both slope coefficients and the threshold parameter. Prediction intervals are also constructed for factor-augmented forecasts. Moreover, we develop a likelihood ratio statistic for tests on the threshold parameter and a sup-Wald test statistic for tests on the presence of threshold effects, respectively. Simulation results show that the proposed estimation method and testing procedures work very well in finite samples. Finally, we demonstrate the usefulness of the proposed model through an application to forecasting stock market returns.

Migration Pattern As Threshold Parameter In The Propagation of The Covid-19 Epidemic Using An Actor-Based Model for SI-Social Graph

Despite the benefits inherent with social interactions, the case of epidemics cum pandemic outbreaks especially the case of the novel corona virus (covid-19) alongside its set protocols employed to contain the spread therein - has continually left the world puzzled as the disease itself has come to stay. The nature of its rapid propagation on exposure alongside its migration spread pattern of this contagion (with retrospect of other epidemics) on daily basis, has also left experts rethinking the set protocols. Our study involved modelling the covid-19 contagion on a social graph, so as to ascertain if its propagation using migration pattern as a threshold parameter can be minimized via the employment of set protocols. We also employed a design that sought to block or minimize targeted spread of the contagion with the introduction of seedset node(s) using the susceptible-infect framework on a time-varying social graph. Study results showed that migration or mobility pattern has become an imperative factors that must be added when modelling the propagation of contagion or epidemics.

AWT - Clustering using an Aggregated Wavelet Tree: A novel automatic unsupervised clustering and outlier detection algorithm for time series

&lt;div&gt;Both clustering and outlier detection play an important role in meteorology. With clustering large sets of data points, such as numerical weather predicition (NWP) model data or observation sites, are separated into groups based on the characteristics found in the data grouping similar data points in a cluster. Clustering enables one, too, to detect outliers in the data. The resulting clusters are useful in many ways such as atmospheric pattern recognition (e.g. clustering NWP ensemble predictions to estimate the likelihood of the predicted weather patterns), climate applications (grouping point observations for climate pattern recognition), forecasting(e.g. data pool enhancement using data of similar sites for forecasting applications), in urban meteorology, air quality, renewable energy systems, and hydrologogical applications.&amp;#160;&amp;#160;&lt;/div&gt;&lt;div&gt;&amp;#160;&lt;/div&gt;&lt;div&gt;Typically, one does not know in advance how many clusters or groups are present in the data. However, for algorithms such as K-means one needs to define how many clusters one wants to have as an outcome. With the proposed novel algorithm&amp;#160;AWT,&amp;#160; a modified combination of several well-known clustering algorithms, this is not needed. It chooses the number of clusters automatically based on a user-defined threshold parameter. Furthermore, the algorithm can be used for heterogeneous meteorological input data as well as data sets that exceed the available memory size.&lt;/div&gt;&lt;div&gt;Similar as the classical BIRCH algorithm, our method AWT works on a multi-resolution data structure, an Aggregated Wavelet Tree that is suitable for representing multivariate time series. In contrast to BIRCH, the user does not need to specify the number of clusters K, as that is difficult in our application. Instead, AWT relies on a single threshold parameter for clustering and outlier detection. This threshold corresponds to the highest resolution of the tree. Points that are not in any cluster with respect to the threshold are naturally flagged as outliers.&lt;/div&gt;&lt;div&gt;&amp;#160;&lt;/div&gt;&lt;div&gt;With the recent increasing usage of non-traditional data sources, such as private, smart-home weather station, in NWP&amp;#160; models and other forecasting and applications outlier and clustering methods are useful in pre-processing and filtering these rather novel data sources. Especially in urban areas changes in the surface energy balance caused by urbanization result in temperatures generally being higher in cities than in the surrounding areas. In order to capture the spatial features of this effect data with high spatial resoltion are necessary. Here, these privately owned smart-home weather stations are useful as often only a limited number of official observation sites exist. However, to be able to use these data they need to be pre-processed.&amp;#160;&amp;#160;&lt;/div&gt;&lt;div&gt;&amp;#160;&amp;#160;&lt;/div&gt;&lt;div&gt;In this work we apply our novel algorithm AWT&amp;#160;to crowdsourced data from the city of Vienna. We demonstrate the skill of the algorithm in outlier detection and filtering as well as clustering the data and evaluate it against commonly used algorithms. Furthermore, we show how one could use the algorithm in renewable energy applications.&lt;/div&gt;

Dynamics of Toxoplasmosis Disease in Cats population with vaccination

We extend the deterministic model for the dynamics of toxoplasmosis proposed by Arenas et al. in 2010, by separating vaccinated and recovered classes. The model exhibits two equilibrium points, the disease-free and endemic steady states. These points are both locally and globally stable asymptotically when the threshold parameter Rv is less than and greater than unity, respectively. The sensitivity analysis of the model parameters reveals that the vaccination parameter $\pi$ is more sensitive to changes than any other parameter. Indeed, as expected the numerical simulations reveal that the higher the vaccination rate of susceptible individuals the smaller the value of the threshold Rv (i.e., increase in $\pi$ results in the decrease in Rv , leading to the eradication of toxoplasmosis in cats population.

Threshold Dynamics in a Model for Zika Virus Disease with Seasonality

We present a compartmental population model for the spread of Zika virus disease including sexual and vectorial transmission as well as asymptomatic carriers. We apply a non-autonomous model with time-dependent mosquito birth, death and biting rates to integrate the impact of the periodicity of weather on the spread of Zika. We define the basic reproduction number $${\mathscr {R}}_{0}$$ R 0 as the spectral radius of a linear integral operator and show that the global dynamics is determined by this threshold parameter: If $${\mathscr {R}}_0 < 1,$$ R 0 < 1 , then the disease-free periodic solution is globally asymptotically stable, while if $${\mathscr {R}}_0 > 1,$$ R 0 > 1 , then the disease persists. We show numerical examples to study what kind of parameter changes might lead to a periodic recurrence of Zika.

A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load

COVID-19 or coronavirus is a newly emerged infectious disease that started in Wuhan, China, in December 2019 and spread worldwide very quickly. Although the recovery rate is greater than the death rate, the COVID-19 infection is becoming very harmful for the human community and causing financial loses to their economy. No proper vaccine for this infection has been introduced in the market in order to treat the infected people. Various approaches have been implemented recently to study the dynamics of this novel infection. Mathematical models are one of the effective tools in this regard to understand the transmission patterns of COVID-19. In the present paper, we formulate a fractional epidemic model in the Caputo sense with the consideration of quarantine, isolation, and environmental impacts to examine the dynamics of the COVID-19 outbreak. The fractional models are quite useful for understanding better the disease epidemics as well as capture the memory and nonlocality effects. First, we construct the model in ordinary differential equations and further consider the Caputo operator to formulate its fractional derivative. We present some of the necessary mathematical analysis for the fractional model. Furthermore, the model is fitted to the reported cases in Pakistan, one of the epicenters of COVID-19 in Asia. The estimated value of the important threshold parameter of the model, known as the basic reproduction number, is evaluated theoretically and numerically. Based on the real fitted parameters, we obtained $\mathcal{R}_{0} \approx 1.50$ R 0 ≈ 1.50 . Finally, an efficient numerical scheme of Adams–Moulton type is used in order to simulate the fractional model. The impact of some of the key model parameters on the disease dynamics and its elimination are shown graphically for various values of noninteger order of the Caputo derivative. We conclude that the use of fractional epidemic model provides a better understanding and biologically more insights about the disease dynamics.

A self-adaptive landmark-based aggregation method for robot swarms

Aggregation, a widely observed behavior in social insects, is the gathering of individuals on any location or on a cue. The former being called the self-organized aggregation, and the latter being called the cue-based aggregation. One of the fascinating examples of cue-based aggregation is the thermotactic behavior of young honeybees. Young honeybees aggregate on optimal temperature zones in the hive using a simple set of behaviors. The state-of-the-art cue-based aggregation method BEECLUST was derived based on these behaviors. The BEECLUST method is a very simple, yet a very capable method that has favorable characteristics such as robustness to noise and simplicity to apply. However, the BEECLUST method does not perform well in low robot densities. In this article, inspired by the navigation techniques used by ants and bees, a self-adaptive landmark-based aggregation method is proposed. In this method, robots use landmarks in the environment to locate the cue once they “learn” the relative position of the cue with respect to the landmark. With the introduction of an error threshold parameter, the method also becomes adaptive to changes in the environment. Through systematic experiments in kinematic and realistic simulators with different parameters, robot densities, and cue sizes, it was observed that using the information of the environment makes the proposed method to show better performance than the BEECLUST in all the settings, including low robot density, high noise, and dynamic conditions.

Spatial dynamics of a diffusive SIRI model with distinct dispersal rates and heterogeneous environment

<p style='text-indent:20px;'>In this paper, we are concerned with the dynamics of a diffusive SIRI epidemic model with heterogeneous parameters and distinct dispersal rates for the susceptible and infected individuals. We first establish the basic properties of solutions to the model, and then identify the basic reproduction number <inline-formula><tex-math id="M1">\begin{document}$ \mathscr{R}_{0} $\end{document}</tex-math></inline-formula> which serves as a threshold parameter that predicts whether epidemics will persist or become globally extinct. Moreover, we study the asymptotic profiles of the positive steady state as the dispersal rate of the susceptible or infected individuals approaches zero. Our analytical results reveal that the epidemics can be extinct by limiting the movement of the susceptible individuals, and the infected individuals concentrate on certain points in some circumstances when limiting their mobility.</p>