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The growing role of artificial intelligence (AI) in our society presents both benefits and challenges. To delve deeper into this, we suggest a simulation model that mimics a human–AI ecosystem. This model considers various factors, such as biased actions and multiple-agent interactions ranging from two to five agents. For instance, it includes situations where four humans engage with AI or where four AI systems interact with a human. Our main goal is to investigate how the number of AI systems in the ecosystem influences its dynamics. Our findings revealed that even slight variations in the presence of AI can cause significant changes in the system's behavior. These shifts can push the system to distinct opposing outcomes or leave it in a state of uncertainty. [ABSTRACT FROM AUTHOR]

Objectives: Ghana's quest to reduce neonatal mortality, in hospital facilities and communities, continues to be a nightmare. The pursuit of achieving healthy lives and well-being for neonates as enshrined in Sustainable Development Goal three lingered in challenging hospital facilities and communities. Notwithstanding that, there have been increasing efforts in that direction. This study examines the contributing factors that hinder the fight against neonatal mortality in all hospital facilities in the Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana. Methods: The study utilized neonatal mortality data consisting of neonatal deaths, structural facility related variables, medical human resources, types of hospital facilities and natal care. The data was collected longitudinally from 2014 to 2019. These variables were analysed using the negative binomial hurdle regression (NBH) model to determine factors that contribute to this menace at the facility level. Cause-specific deaths were obtained to determine the leading causes of neonatal deaths within health facilities in the two municipal assemblies. Results: The study established that the leading causes of neonatal mortality in these districts are birth asphyxia (46%), premature birth (33%), neonatal sepsis (11%) and neonatal jaundice (7%). The NBH showed that neonatal mortality in hospital facilities depend on the number of incubators, monitoring equipment, hand washing facilities, CPAPb machines, radiant warmers, physiotherapy machines, midwives, paediatric doctors and paediatric nurses in the hospital facility. Conclusions: Early management of neonatal sepsis, birth asphyxia, premature birth and neonatal infections is required to reduce neonatal deaths. The government and all stakeholders in the health sector should provide all hospital facilities with the essential equipment and the medical human resources necessary to eradicate the menace. This will make the realization of Sustainable Development Goal three, which calls for healthy lives and well-being for all, a reality. [ABSTRACT FROM AUTHOR]

In recent times, there have been a lot of advocacies to encourage females of all ages to pursue education to the highest level. The Government of Ghana and its stakeholders have championed this to achieve the Sustainable Development Goal 4 in this regard. However, progress has been on a snail's pace. In this study, we employed the multipopulation Curie–Weiss model to establish the influence of sociodemographic factors on the educational attainment of all females in Ghana. We utilized data from the Ghana Demographic and Health Survey (2008 and 2014). The parameters of the interacting and noninteracting multipopulation Curie–Weiss models were estimated using the partial least squares and ordinary least squares methods, respectively. Our findings suggest that marital status, place of residence, and wealth status of females in Ghana influence their educational attainment. Therefore, to get more females of all ages to attain higher levels of education, the Government of Ghana and its stakeholders should channel their efforts towards eradicating poverty at all levels, preventing early and teenage marriages, providing better infrastructure, and creating better living conditions for all females especially those in the poorest regions of the country. [ABSTRACT FROM AUTHOR]

Studies have indicated that the risk of malaria, particularly its association with anaemia in pregnant women, increases when treated bed nets are not used. This paper utilizes a statistical mechanical model to investigate whether there is a statistical relationship between the presence or absence of anaemia in pregnant and non-pregnant women and their decision to sleep under treated bed nets. Data from the Ghana Malaria Indicator Survey (GMIS), which includes both rural and urban malaria-endemic areas in Ghana, were employed in this study. A total of 2,434 women, comprising 215 pregnant and 2,219 non-pregnant participants, were involved. Among these, 4.76% of the pregnant and anaemic women and 45.89% of the non-pregnant and anaemic women slept under treated bed nets, while 0.86% of the pregnant and anaemic and 6.82% of the non-pregnant and anaemic women did not. The findings revealed that, in the absence of social interaction, non-anaemic pregnant women have a lower prevalence of choosing to use bed nets compared to their anaemic counterparts. Additionally, non-pregnant anaemic women showed a positive private incentive (30.87%) to use treated bed nets, implying a positive correlation between anaemia and the choice to sleep in a treated bed net. Furthermore, the study demonstrated that both pregnancy and anaemia status have a relationship with the use of treated bed nets in Ghana, especially when social interactions are considered. The interaction strength between non-pregnant and anaemic women interacting with each other shows a negative estimate (-1.49%), implying that there is no rewarding effect from imitation. These insights are crucial for malaria prevention and control programs, emphasizing the need for targeted interventions to enhance the use of treated bed nets among both pregnant and non-pregnant women in Ghana's malaria-endemic regions. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) emissions have become a critical aspect of the economic and sustainable development indicators of every country. In Pakistan, where there is a substantial increase in the population, industrialization, and demand for electricity production from different resources, the fear of an increase in CO2 emissions cannot be ignored. This study explores the link that betwixt CO2 emissions with different significant economic indicators in Pakistan from 1960 to 2018 using the autoregressive distributed lag (ARDL) modelling technique. We implemented the covariance proportion, coefficient of determination, the Durbin Watson D statistics, analysis of variance (ANOVA), variance inflating factor (VIF), the Breusch-Pagan test, the Theil's inequality, the root mean quare error (RMSE), the mean absolute percentage error (MAPE), and the mean absolute error (MAE) for the diagnostics, efficiency, and validity of our model. Our results showed a significant association between increased CO2 emissions and increased electricity production from oil, gas, and other sources. An increase in electricity production from coal resources was seen to have resulted in a decrease in CO2 emissions. We observed that an increase in the gross domestic product (GDP) and population growth significantly contributed to the increased CO2 emissions. The increment in CO2 emissions resulting from industrial growth was not significant. The increment in CO2 emissions in the contemporary year is significantly associated with the preceding year's increase. The rate of increase was very alarming, a sign that no serious efforts have been channelled in this regard to reduce this phenomenon. We call for policy dialogue to devise energy-saving and CO2 emission reduction strategies to minimize the impact of climate change on industrialization, population growth, and GDP growth without deterring economic and human growth. Electricity production from different sources with no or minimal CO2 emissions should be adopted. We also recommend rigorous tree planting nationwide to help reduce the concentration of CO2 in the atmosphere as well as environmental pollution. [ABSTRACT FROM AUTHOR]

Pakistan is considered among the top five countries with the highest CO2 emissions globally. This calls for pragmatic policy implementation by all stakeholders to bring finality to this alarming situation since it contributes greatly to global warming, thereby leading to climate change. This study is an attempt to make a comparative analysis of the linear time series models with nonlinear time series models to study CO2 emission data in Pakistan. These linear and nonlinear time series models were used to model and forecast future values of CO2 emissions for a short period. To assess and select the best model among these linear and nonlinear time series models, we used the root mean square error (RMSE) and the mean absolute error (MAE) as performance indicators. The outputs showed that the nonlinear machine learning models are the best among all other models, having the lowest RMSE and MAE values. Based on the forecasted value of the nonlinear machine learning neural network autoregressive model, Pakistan's CO2 emissions will be 1.048 metric tons per capita by 2028. The increasing trend in emissions is a frightening and clear warning, suggesting that innovative policies must be initiated to reduce the trend. We encourage the Pakistan government to price CO2 emissions by companies and entities per ton, adapt electricity production from hydro, wind, and different sources with no emissions of CO2, initiate rigorous planting of more trees in the populated areas of Pakistan as forest covers, provide incentives to companies, organisations, institutions, and households to come out with clean technologies or use technologies with no CO2 emissions or those with lower ones, and fund more studies to develop clean and innovative technologies with less or no CO2 emissions. [ABSTRACT FROM AUTHOR]

Background: Awareness of rights is a precondition to establishing rule of law in society. Sexual and Reproductive Health and Rights (SRHR) are closely knitted in the human rights framework as they overlap with other human rights such as the right to health and life. However, awareness about these rights remains a challenge. Considering the importance of these rights this study has measured the awareness of adolescents about SRHR in Bahawalpur (Division) of Pakistan. Methodology: We conducted a cross‐sectional study in the Divison of Bahawalpur, Pakistan from October 2019 to December 2019. The study included a sample size of 500 respondents which included 250 young females of age 15–19 and 250 their parents (mother). The reasoning behind including only females in this study was the increased vulnerability and greater impact of SRHR in their life compared to males. The age range 15–19 was primarily selected as females this age will better understand and respond to the questionnaire compared to females in their early adolescent years. The quantitative research was conducted using two‐stage cluster sampling. Detailed structured questionnaires were distributed among the respondents to obtain their points of view on the awareness of SRHR. The population was divided into multi‐clusters with 25 households comprising 250 households for the division of Bahawalpur. The information was also gathered from the doctors and the teachers through interviews. The data were analyzed using SPSS version 21. Results: The study explored the knowledge and understanding of adolescent SRHR thereby highlighting the key restrictions in Bahawalpur, Pakistan, which prohibit adolescents from gaining access to SRHR and exercising it. There is a significant portion of adolescents who strongly agreed with the importance and awareness level and think they should be more aware of information regarding SRHR. However, they are of the view that they are less independent in practicing them. Conclusion: The study found a low level of awareness about SRHR among young female and their parents in Bahawalpur, Pakistan. It is a need of time and responsibility of the local government of the Bahawalpur region to devise clear and proper policies which give access to these rights. This can be done by including the information on these rights in the course curriculum and teachers keeping in view the cultural and regional restrictions that guide the young female about SRHR. [ABSTRACT FROM AUTHOR]

Pakistan is considered among the top five countries with the highest CO 2 emissions globally. This calls for pragmatic policy implementation by all stakeholders to bring finality to this alarming situation since it contributes greatly to global warming, thereby leading to climate change. This study is an attempt to make a comparative analysis of the linear time series models with nonlinear time series models to study CO 2 emission data in Pakistan. These linear and nonlinear time series models were used to model and forecast future values of CO 2 emissions for a short period. To assess and select the best model among these linear and nonlinear time series models, we used the root mean square error (RMSE) and the mean absolute error (MAE) as performance indicators. The outputs showed that the nonlinear machine learning models are the best among all other models, having the lowest RMSE and MAE values. Based on the forecasted value of the nonlinear machine learning neural network autoregressive model, Pakistan's CO 2 emissions will be 1.048 metric tons per capita by 2028. The increasing trend in emissions is a frightening and clear warning, suggesting that innovative policies must be initiated to reduce the trend. We encourage the Pakistan government to price CO 2 emissions by companies and entities per ton, adapt electricity production from hydro, wind, and different sources with no emissions of CO 2 , initiate rigorous planting of more trees in the populated areas of Pakistan as forest covers, provide incentives to companies, organisations, institutions, and households to come out with clean technologies or use technologies with no CO 2 emissions or those with lower ones, and fund more studies to develop clean and innovative technologies with less or no CO 2 emissions., Competing Interests: The authors declare that there have no conflicts of interest., (Copyright © 2023 Kassim Tawiah et al.)