Lymphatic filariasis is a leading cause of chronic and irreversible damage to human immunity. This paper presents deterministic and continuous-time Markov chain (CTMC) stochastic models regarding lymphatic filariasis dynamics. To account for randomness and uncertainties in dynamics, the CTMC model was formulated based on deterministic model possible events. A deterministic model’s outputs suggest that disease extinction is feasible when the secondary threshold infection number is below one, while persistence becomes likely when the opposite holds true. Furthermore, the significant contribution of asymptomatic carriers was identified. Results indicate that persistence is more likely to occur when the infection results from asymptomatic, acutely infected, or infectious mosquitoes. Consequently, the CTMC stochastic model is essential in capturing variabilities, randomness, associated probabilities, and validity across different scales, whereas oversimplification and unpredictability of inherent may not be featured in a deterministic model.

Lymphatic filariasis represents the primary cause of long-term, permanent disability, and dysfunction in the human immune system. In this study, we have devised and assessed deterministic and continuous-time Markov chain (CTMC) stochastic models to gain insights into the dynamics of lymphatic filariasis and approximate the probabilities of disease extinction or outbreak. The CTMC stochastic model is an adapted version of the existing deterministic model that accounts for uncertainties and variations in disease transmission dynamics. The findings from the deterministic model indicate that disease extinction is possible when ℛ_0 < 1 , while an outbreak is likely when ℛ_0 > 1 . Further examination of the deterministic model emphasizes the significant role of asymptomatic individuals in the transmission of lymphatic filariasis. To estimate the probabilities of disease extinction or outbreak, we employed multitype branching processes and numerical simulations. The results demonstrate that lymphatic filariasis outbreaks are more probable when microfilariae parasites are introduced by exposed humans, asymptomatic humans, acutely infected humans, exposed mosquitoes, or infectious mosquitoes. Conversely, the disease is more likely to be eradicated if it originates from chronically infected humans. Utilizing stochastic methods provides a more authentic portrayal of how lymphatic filariasis spreads, granting a better understanding of the spectrum of potential results and their related probabilities. Therefore, stochastic CTMC models become indispensable for generating reliable forecasts and well-informed choices in situations where deterministic models might oversimplify or inaccurately depict the inherent unpredictability.

Education is one of the social spheres that today is actively transforming under the pressure of external and internal factors. The Covid19 pandemic has contributed to the active use of digital technologies in educational institutions all around the world. This study examines the future of education through the fictional stories of students (N = 196) about how they see education in 2050. The “future” in the stories is formed primarily due to a more advanced technological environment than today. Students especially often described “alarm clock” technologies (40

The 1990s was a time of discovery of digital technology by mass audiences. Russian schoolchildren of the 1990s had their first experience with electronic games on personal devices, which for many turned out to be a meaningful childhood memory. The article analyzes stories about first gaming experiences, interactions with technology, and parental control of this period, supplemented by interviews with some of the authors of the posts. Although the impact of technology had not been studied yet, parents were concerned about their children’s excessive fascination with playing games on consoles and early computers and tried to limit their children’s use of technology. In the parent-child-technology triangle, parents sought to weaken the child-technology relationship primarily by influencing the technology - by compromising hardware integrity or setting software limits so that children could not play in the absence of their elders. Such actions, judging from memories, only served to encourage the pupils, who showed marvels of ingenuity and cunning in their desire to play. Parental control had a specific effect, usually not by interrupting the child-technology relationship, but by stimulating a deeper understanding of its structure.

Cryptosporidiosis is a zoonotic disease caused by Cryptosporidium. The disease poses a public and veterinary health problem worldwide. A deterministic model and its corresponding continuous time Markov chain (CTMC) stochastic model are developed and analyzed to investigate cryptosporidiosis transmission dynamics in humans and cattle. The basic reproduction number R 0 for the deterministic model and stochastic threshold for the CTMC stochastic model are computed by the next generation matrix method and multitype branching process, respectively. The normalized forward sensitivity index method is used to determine the sensitivity index for each parameter in R 0 . Per capita birth rate of cattle, the rate of cattle to acquire cryptosporidiosis infection from the environment and the rate at which infected cattle shed Cryptosporidium oocysts in the environment play an important role in the persistence of the disease whereas Cryptosporidium oocysts natural death rate, cattle recovery rate and cattle natural death rate are most negative sensitive parameters in the dynamics of cryptosporidiosis. Numerical results for CTMC stochastic model show that the likelihood of cryptosporidiosis extinction is high when it arises from an infected human. However, there is a major outbreak if cryptosporidiosis emerges either from infected cattle or from Cryptosporidium oocysts in the environment or when it emerges from all three infectious compartments. Therefore to control the disease, control measures should focus on maintaining personal and cattle farm hygiene and decontaminating the environment to destroy Cryptosporidium oocysts.

Despite having been tested in multiple settings, the quantitative impact of predatory aquatic insects to reduce mosquito population remains unclear. To address this question, an ecological model of Aedes mosquito population incorporating temperature-dependent entomological parameters and predation is developed. The vector reproduction number is derived and entomological parameters that strongly influence it have been identified. Implications of predation on mosquito growth are examined. Results show that predators with high daily attack rate can significantly reduce vector reproduction to extremely low values close to zero. The study provides a framework for more detailed, predator specific studies that are essential to develop an improved understanding on the relationship between predatory aquatic insects and Aedes mosquitoes.

In today's world filled with complex signs and symbols, visual and auditory channels are the most intensive in semiotic terms. The language of smell, associated with the most ancient reactions, is usually considered as secondary and supplementary, and its possibilities for conveying meanings are limited to simple recognition. However, experts have been using the alphabet of smells to convey emotional messages from ancient times to date. The assessment of the role of odors in the modern world became possible due to the Covid-19 pandemic which often involved the loss, change or intensification of the sense of smell. In the course of the study 250 cases were considered, representing the stories associated with the disease and deviations in the perception of odors. The loss of the perception of unpleasant odors makes it impossible to learn about the dangers which cannot be perceived visually like in ancient times (spoiled food, poisoned air, etc.). Phantom interpretation of odors is often unpleasant: people can identify the smells of burning, ammonia, acetone, decomposition, feces, and others, and sometimes the excessiveness of an ordinary smell is unpleasant as well. The change of sign recognition can cause serious consequences for people. Phantom unpleasant odors can result in changes in eating habits and cause problems in communication.

Lymphatic filariasis is a neglected tropical disease which poses public health concern and socio-economic challenges in developing and low-income countries. In this paper, we formulate a deterministic mathematical model for transmission dynamics of lymphatic filariasis to generate data by white noise and use least square method to estimate parameter values. The validity of estimated parameter values is tested by Gaussian distribution method. The residuals of model outputs are normally distributed and hence can be used to study the dynamics of Lymphatic filariasis. After deriving the basic reproduction number, R0 by the next generation matrix approach, the Partial Rank Correlation Coefficient is employed to explore which parameters significantly affect and most influential to the model outputs. The analysis for equilibrium states shows that the Lymphatic free equilibrium is globally asymptotically stable when the basic reproduction number is less a unity and endemic equilibrium is globally asymptotically stable when R0≥1. The findings reveal that rate of human infection, recruitment rate of mosquitoes increase the average new infections for Lymphatic filariasis. Moreover, asymptomatic individuals contribute significantly in the transmission of Lymphatic filariasis.

In recent decades, media campaigns have played an important role in assessing, preventing and controlling infectious diseases. However, little progress has been made in quantifying its impact during chikungunya epidemics. In order to address this critical gap, this paper develops and analyzes a climate-based model of chikungunya virus disease that incorporates mass media campaigns and heterogeneous biting exposures. We obtained the basic reproduction numbers associated with the proposed model and determined the results of the threshold dynamics. We calibrated our model based on literature data and validated it with monthly observed chikungunya cases in Madhya Pradesh, India (2016–2017). The results show that mass media campaigns can significantly reduce the spread of the disease. It can also limit the occurrence of future outbreaks in the next few years. We also observed that media fatigue may reduce the impact of media to mitigate the spread of chikungunya virus.

Approximately 1.3 billion inhabitants in 94 countries are estimated to be at risk of chikungunya virus infection. A mechanistic compartmental model based on fractional calculus, the Caputo derivative has been proposed to evaluate the effects of temperature and multiple disease control measures (larvicides use, insecticides and physical barriers) during an outbreak. The proposed model was calibrated based on data from literature and validated with daily chikungunya fever cases reported at Kadmat primary health centre, India. The transmission potential of the disease was examined. Sensitive analyses were conducted through computing partial rank correlation coefficients. Memory effects which are often neglected when mechanistic models are used to model the transmission dynamics of infectious diseases, were found to have a significant effect on the dynamics of chikungunya.

Bovine cysticercosis and human taeniasis are neglected food-borne diseases that pose challenge to food safety, human health and livelihood of rural livestock farmers. In this paper, we have formulated and analyzed a deterministic model for transmission dynamics and control of taeniasis and cysticercosis in humans and cattle respectively. The analysis shows that both the disease free equilibrium (DFE) and endemic equilibrium (EE) exist. To study the dynamics of the diseases, we derived the basic reproduction number R 0 by next generation matrix method which shows whether the diseases die or persist in humans and cattle. The diseases clear if R 0 < 1 and persist when R 0 > 1. The normalized forward sensitivity index is used to derive sensitive indices of model parameters. Sensitivity analysis results indicate that human's and cattle's recruitment rates, infection rate of cattle from contaminated environment, probability of humans to acquire taeniasis due to consumption of infected meat, defecation rate of humans with taeniasis and the consumption rate of raw or undercooked infected meat are the most positive sensitive parameters whereas the natural death rates for humans, cattle, Taenia saginata eggs and the proportion of unconsumed infected meat are the most negative sensitive parameters in diseases' transmission. These results suggest that control measures such as improving meat cooking, meat inspection and treatment of infected humans will be effective for controlling taeniasis and cysticercosis in humans and cattle respectively. The optimal control theory is applied by considering three time dependent controls which are improved meat cooking, vaccination of cattle, and treatment of humans with taeniasis when they are implemented in combination. The Pontryagin's maximum principle is adopted to find the necessary conditions for existence of the optimal controls. The Runge Kutta order four forward-backward sweep method is implemented in Matlab to solve the optimal control problem. The results indicate that a strategy which focuses on improving meat cooking and treatment of humans with taeniasis is the optimal strategy for diseases' control.

Bovine cysticercosis and human taeniasis are neglected food-borne infections that pose challenges to food safety, peoples' health and the economy of rural farmers who depend on livestock keeping. In this paper, the continuous time Markov chain (CTMC) stochastic model is formulated based on its corresponding deterministic model and rigorously analyzed to study the dynamics of cysticercosis in cattle and taeniasis in humans. The multitype branching process is adopted to compute the stochastic threshold and numerical simulations for the CTMC model with 10,0 0 0 sample paths are used to compute probabilities of diseases' extinction when initial conditions for infected classes are varied. The results show that when there is disease outbreak, the solutions of CTMC stochastic model are relatively close to deterministic model solutions. The analytical and numerical results for probability of diseases' extinction are in good agreement when initially there is no infectious human. Some variations in probability of diseases' extinction are due to infectious beef being considered a discrete number in CTMC model. The results show further that the diseases' extinction probability is high when the diseases emerge from a small number of T. saginata eggs or from infected cattle. However, the probability becomes low when there is at least an infectious beef at the beginning of the diseases' outbreak. (c) 2022 Elsevier Inc. All rights reserved.

Within the last decades, chikungunya virus (CHIKV), a mosquito-borne arboviral disease transmitted by Aedes species mosquitoes has been a growing public health burden. Approximately 1.3 billion inhabitants in 94 countries are estimated to be at risk of chikungunya virus infection. Prior studies suggest that temperature and heterogeneous biting exposure are some of the key determinant factors in transmission dynamics of vector-borne diseases. In order to direct preparedness for future outbreaks, it is imperative to evaluate the effects of heterogeneous biting exposure and temperature variations on transmission dynamics of CHIKV. In this paper, a mathematical model that incorporates heterogeneous biting exposure and temperature effects has been developed and analyzed. The basic reproduction number, an important metric for infectious disease models has been determined. Data from literature has been used to calibrate the model and observed CHIKV data for Kadmat primary health centre, India (2 July to 7 September 2007) has been used to validate the model. Results from the study suggest that disease prevention strategies which are effective at stopping transmission of CHIKV more than 80% of the time, will be highly effective minimizing disease burden during outbreaks. The proposed model can be used to inform policy makers on effective ways of managing CHIKV during outbreaks.

Modern commercial antivirus systems increasingly rely on machine learning (ML) to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus (AV) products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. We propose a black-box Reinforcement Learning (RL) based framework to generate AEs for PE malware classifiers and AV engines. It regards the adversarial attack problem as a multi-armed bandit problem, which finds an optimal balance between exploiting the successful patterns and exploring more varieties. Compared to other frameworks, our improvements lie in three points: 1) limiting the exploration space by modeling the generation process as a stateless process to avoid combination explosions, 2) reusing the successful payload in modeling; and 3) minimizing the changes on AE samples to correctly assign the rewards in RL learning (which also helps identify the root cause of evasions). As a result, our framework has much higher evasion rates than other off-the-shelf frameworks. Results show it has over 74%--97% evasion rate for two state-of-the-art ML detectors and over 32%--48% evasion rate for commercial AVs in a pure black-box setting. We also demonstrate that the transferability of adversarial attacks among ML-based classifiers is higher than that between ML-based classifiers and commercial AVs.

Modern commercial antivirus systems increasingly rely on machine learning (ML) to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus (AV) products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. We propose a black-box Reinforcement Learning (RL) based framework to generate AEs for PE malware classifiers and AV engines. It regards the adversarial attack problem as a multi-armed bandit problem, which finds an optimal balance between exploiting the successful patterns and exploring more varieties. Compared to other frameworks, our improvements lie in three points: 1) limiting the exploration space by modeling the generation process as a stateless process to avoid combination explosions, 2) reusing the successful payload in modeling; and 3) minimizing the changes on AE samples to correctly assign the rewards in RL learning (which also helps identify the root cause of evasions). As a result, our framework has much higher evasion rates than other off-the-shelf frameworks. Results show it has over 74%-97% evasion rate for two state-of-the-art ML detectors and over 32%-48% evasion rate for commercial AVs in a pure black-box setting. We also demonstrate that the transferability of adversarial attacks among ML-based classifiers is higher than that between ML-based classifiers and commercial AVs.

Maize remains an important food crop in Africa. However, the production of this crop, and consequently the livelihood of the growers are threatened by the invasion and widespread infestation of the fall armyworm which causes substantial maize yield losses. In this paper, a fractional-order fall armyworm-maize biomass model with naturally beneficial insects and optimal farming awareness has been formulated. Comprehensive analysis of the model has shown that it contains five equilibrium points which are all locally and globally asymptotically stable if the conditions outlined in Lemma 2.1 and 2.2 are met. We also carried out numerical simulations to support the analytical results and to illustrate different dynamical regimes that can be observed in the model. We have found that time-dependent farming awareness can significantly reduce fall armyworm population if the cost of implementation is relatively low.

In the modern technological world, the concept of creativity is associated with a timeline and a focus on the future. The accelerating change of life under the influence of high-tech changes requires new approaches to the discourse about the future. Creativity as the most “lively” and unpredictable component of technological development is difficult to define, measure, and evaluate. The rapid development of technologies has brought the creation of ideas and their implementation much closer. The most popular issue is becoming the discourse about the future in the format of foresights, which represent the programming of the future. In this study, the authors propose a direction for generating ideas other than tasks for evaluating individual divergent thinking and the foresight format. The assumption of the absence of one of the basic foundations of the existence of modern humanity was used as an incentive to generate ideas about the fate of our civilization, its problems, and opportunities. Understanding the impact of technology development on society, the ability to assess the consequences of technological decisions is an important competence of a modern engineer. The article presents a general picture of the student's answers to the question about the consequences of the absence of gravity (N = 100) and electricity (N = 150). Along with disaster scenarios, students present the solutions for adaptation, positive consequences, and options for using new opportunities. The answers trace the influence of mass culture, popular media discourse, scientific facts related to the topic, and everyday experience, which in some cases are evaluated and revised in a new context.

Fall armyworm ( Spodoptera frugiperda ), a highly destructive and fast spreading agricultural pest native to North and South America, poses a real threat to global food security. In this paper, to explore the dynamics and implications of fall armyworm outbreak in a field of maize biomass, we propose a new dynamical system for maize biomass and fall armyworm interaction via Caputo fractional-order operator, which is not only a nonlocal operator but also contains all characteristics concerned with memory of the dynamical system. We define the basic reproduction number, which represents the average number of newborns produced by one individual female moth during its life span. We establish that the basic reproduction number is a threshold quantity, which determines persistence and extinction of the pest. Finally, we simulate the Caputo system using the Adam–Bashforth–Moulton method to illustrate the main results.

In this study, we present a non-autonomous model with a Holling type II functional response, to study the complex dynamics for fall armyworm-maize biomass interacting in a periodic environment. Understanding how seasonal variations affect fall armyworm-maize dynamics is critical since maize is one of the most important cereals globally. Firstly, we study the dynamical behaviours of the basic model; that is, we investigate positive invariance, boundedness, permanence, global stability and non-persistence. We then extended the model to incorporate time dependent controls. We investigate the impact of reducing fall armyworm egg and larvae population, at minimal cost, through traditional methods and use of chemical insecticides. We noted that seasonal variations play a significant role on the patterns for all fall armyworm populations (egg, larvae, pupae and moth). We also noted that in all scenarios, the optimal control can greatly reduce the sizes of fall armyworm populations and in some scenarios, total elimination may be attained. The modeling approach presented here provides a framework for designing effective control strategies to manage the fall armyworm during outbreaks.