Discrete Dynamics in Nature and Society

The dengue virus is the most dangerous one that mosquitoes may spread to people. Despite attempts by the government, dengue outbreaks are becoming increasingly common in Bangladesh. Interventions in public health rely heavily on KAP (knowledge, attitude, and practice) studies. The primary goal of this research is to forecast the occurrence of dengue disease in the city of Dhaka using methods from machine learning and time series analysis and then to compare the models in order to find the one with the lowest MAPE. From January 2016 through July 2021, monthly data were retrieved for this study from WHO and the Directorate General of Health and Services (DGHS). According to the findings of this research, neural networks outperform time series analysis when it comes to making predictions. The best-fitted neural network (NN) model was found in model 04 with 05 hidden layers which produced the minimum error model with the value of error 0.003032557, and the values of RMSE and MAPE are 7.588889e − 06 and 1.15273, respectively, for the prediction of the dengue fever in Dhaka city. In contrast, the original dengue data in the time series analysis is not stationary. Take the difference and run the unit root test by the augmented Dickey–Fuller (ADF) test to make it stationary. The dengue data series is stationary at the first-order difference, as evidenced by the ACF and PACF, which show no noticeable spike in the first-order difference. The ARIMA (6, 1, 1) model with the lowest AIC = −251.8, RMSE = 0.0310797, and MAPE = 15.2892 is the best choice model for predicting the dengue death rate. Therefore, from these two models, the NN model gives better prediction performance with the lowest value of MAPE. So, the neural network gives better prediction performance than time series analysis. The NN model forecasted 12-month death rates of dengue fever that suggest the death rate in dengue fever falling month by month. This study is more innovative than any other research because this research approach is different from any other research approach. The model selection criteria are based on the most effective performance metrics MAPE, indicating the lowest error and better prediction performance. Therefore, from this research, the author suggests machine learning gives better prediction performance than time series analysis for any other prediction performance.

In this paper, the limit cycles and local bifurcation of critical periods for a class of switching equivariant quartic system with two symmetric singularities are investigated. First, through the computation of Lyapunov constants, the conditions of the two singularities to become the centers are determined. Then, we prove that there are at most 18 limit cycles with a distribution pattern of 9-9 around the two symmetric singular points of the system. Numerical simulation is conducted to validate the obtained results. Furthermore, by calculating the period constants, we determine the conditions for the critical point to be a weak center of finite order. Finally, the number of local critical periods that bifurcate from the equilibrium point under the center conditions is discussed. This study presents the first example of a quartic switching smooth system with 18 limit cycles and 4 local critical periods bifurcating from two symmetric singular points.

Banks are financial institutions that are crucial to the accomplishment of development objectives because they transfer money from surplus to deficit parties. This study examines the competition, survival, and performance constraints of commercial banks in Ethiopia using the DEA and CAMEL frameworks from 2015 to 2020. The nonparametric (DEA) approach was applied to approximate the overall technical efficiency score of the banks under consideration. The empirical study used twelve private commercial banks operating in Ethiopia, excluding four banks because of a lack of appropriate and audited financial data and no risk assessment between the study periods. The result of the study reveals that under CRS, except for Abyssinia, Anbesa, and Nib International Banks, the remaining banks are more efficient. Under VRS, while Abyssinia Bank was less efficient, other banks were found to be more efficient. Under scale efficiency, Abyssinia, Anbesa, and Nib International Banks were found to be less efficient, while the remaining banks were more efficient. The composite ranking of the CAMEL framework portrays that Awash International Bank, Zemen, and Wegagen Banks were found in the top three ranks, while Cooperative Bank of Oromia, Dashen, and Abyssinia Banks were found in the bottom position. The regression result demonstrates that the ratios of total capital to total assets, loans to assets, total loans to total deposits, and CAR have a positive effect on bank profitability, whereas the ratio of total loans to the number of branches has a statistically negative and significant influence on the return on assets.

This paper examines the dynamic linkages among economic policy uncertainty (EPU), the green bond market, the carbon market, and the macroeconomy using the time-varying parameter vector autoregressive (TVP-VAR) model with monthly data spanning from January 2016 to December 2021. Additionally, it assesses the robustness and accuracy of the empirical results through the Bayesian vector autoregressive (BVAR) model. The findings indicate that EPU negatively affects the green bond market in the short term but has a positive impact in the medium and long term. Conversely, EPU has a positive impact on the carbon market in the short term but a negative impact in the medium and long term. Furthermore, the green bond market negatively influences the carbon market in both the short and medium to long term. These results suggest that emerging markets, such as the green bond and carbon markets, are influenced by EPU. The adverse impact of the green bond market on the carbon market, however, contributes to expediting China’s attainment of its low-carbon objectives. Appropriate economic policies can play a vital role in accelerating the transition to a low-carbon economy. The study also reveals that the US-China trade war has expedited the development of green capital markets in China, despite its impact on the green economic transition in the country. These findings provide insights for the government and investors to formulate suitable strategies for risk mitigation.

How does foreign aid affect recipient countries’ labor institutions? Extant empirical evidence is sparse due to ambiguous theoretical predictions and potential endogeneity issues. This study uses data detailing aid flow and institutional functions to mitigate such inconclusiveness and displays originality by constructing a concise theoretical framework in which foreign aid contributes to the improvement of the labor institutions in recipient countries through two mechanisms—economic growth purpose and expected aid attraction. The findings indicate that only aid to enhance local economic growth provides incentives to change domestic labor markets. Such effects have a more crucial influence on labor institutions of minimum wage, collective bargaining rights, and working hours than those related to hiring and firing regulations, mandated costs for worker dismissal, and conscription. For comparison with the actual aid level, we construct forecasted proxies to capture exogenous fluctuations in aid, determining that domestic labor markets can also change via a novel mechanism, as the recipient voluntarily mimics the funder’s labor policies in anticipation of receiving future aid, with exact amounts derived from the noninstitutional characteristics of pairs of bilateral recipient and giver economies. Therefore, we provide policy implications for the aid giver on how to secure a continued and increased aid flow and for the aid receivers on which aspects of reforming measures are most effective in enhancing the labor market regulations.

Pulmonary tuberculosis is among the leading infectious diseases causing mortality worldwide. Therefore, scaling up intervention strategies to reduce the spread of infections in the population is imperative. In this paper, a population-based compartmental approach has been employed to formulate a mathematical model of pulmonary tuberculosis that incorporates an asymptomatic infectious population. The model includes asymptomatic infectious individuals since they spread infections incessantly to susceptible populations without being noticed, thus contributing to the high rate of infection transmission. Qualitative and numerical analyses were performed to determine the impact of various intervention strategies on controlling infection transmission in the population. Sensitivity and numerical results indicate that increasing screening of latently infected and asymptomatic infectious individuals reduces infection transmission to the susceptible population. Numerical results demonstrate that the combination of vaccination, screening, and treatment of all forms of pulmonary tuberculosis is the most effective intervention in decreasing infection transmission. Furthermore, a combination of screening and treatment of all forms of pulmonary tuberculosis proves more effective than a combination of vaccination and treatment of symptomatic infectious individuals alone. Treating the symptomatic infectious population alone is identified as the least effective intervention for curtailing infection transmission in the susceptible population. These study findings will guide healthcare officials in making decisions regarding the screening of latently infected and asymptomatic infectious pulmonary tuberculosis patients, thereby aiding in the fight against epidemics of this disease.