Seemingly unrelated time series model for forecasting the peak and short-term electricity demand: Evidence from the Kalman filtered Monte Carlo method.

In this extant paper, a multivariate time series model using the seemingly unrelated times series equation (SUTSE) framework is proposed to forecast the peak and short-term electricity demand using time series data from February 2, 2014, to August 2, 2018. Further the Markov Chain Monte Carlo (MCMC) method, Gibbs Sampler, together with the Kalman Filter were applied to the SUTSE model to simulate the variances to predict the next day's peak and electricity demand. Relying on the study results, the running ergodic mean showed the convergence of the MCMC process. Before forecasting the peak and short-term electricity demand, a week's prediction from the 28th to the 2nd of August of 2018 was analyzed and it found that there is a possible decrease in the daily energy over time. Further, the forecast for the next day (August 3, 2018) was about 2187 MW and 44090 MWh for the peak and electricity demands respectively. Finally, the robustness of the SUTSE model was assessed in comparison to the SUTSE model without MCMC. Evidently, SUTSE with the MCMC method had recorded an accuracy of about 96% and 95.8% for Peak demand and daily energy respectively.

Teamwork quality and health workers burnout nexus: a new insight from canonical correlation analysis.

BACKGROUND: Burnout is evidenced to have  adverse effect on the well-being of health workers. Although several risk factors of burnout have been found, only a hand full of studies have examined the role of teamwork quality. This study therefore sought to explore the relationship between the sub-dimensions of burnout and teamwork quality. METHOD: This is an empirical study involving health workers who have practising certificate from the National Health Commission of the People's Republic of China. Relying on the study's target population, a sample of 939 healthworkers complied to partake in the survey. Data were obtained from the administration of a well-structured electronic questionnaire containing the Maslach Burnout Inventory together with Healthy and Resilient Organization (HERO) scales correspondingly. The scales were then analysed using the canonical correlation approach (CCA). RESULTS: The results unveiled a statistically significant correlation between teamwork quality and health worker burnout indicating that teamwork quality and burnout are canonically correlated. Further, examination on the relationship existing between the dimensions of teamwork quality and burnout unveiled that with the exception of personal accomplishment and teamwork dedication, teamwork quality sub-scales (teamwork vigour and teamwork absorption) were negatively related to emotional exhaustion and depersonalization as sub-scales of burnout, respectively. CONCLUSION: The study concluded that, surge in teamwork quality leads to reduced emotional exhaustion and reduced depersonalization while simultaneously increasing professional accomplishment. Therefore, this study presents a solid foundation for decreasing burnout syndrome in healthcare that can be implemented by successfully increasing levels of teamwork quality.

Reinvestigating the pollution haven hypothesis: the nexus between foreign direct investments and environmental quality in G-20 countries.

One of the most commonly debated concerns regarding foreign direct investment inflows is the associated environmental adversities that accompany the influx of foreign funds. As a result, assessing the environmental impacts of foreign direct investment inflows is necessary for achieving environmentally friendly economic growth in the contemporary era. Accordingly, the global economies including the members of the Group of Twenty (G-20) should focus on attracting clean foreign direct investments. Against this backdrop, controlling for energy consumption and urbanization, this extant study scrutinizes the effects of foreign direct investment inflows on the carbon dioxide emission figures of selected G-20 countries between 1992 and 2018. The econometric analysis conducted in this paper involves recently developed methods that are efficient in handling cross-sectionally dependent heterogeneous panel data sets. Besides, the analysis is also conducted for sub-panels of high-, upper-middle-, and lower-middle-income G-20 countries to evaluate the possible heterogeneous environmental effects across the G-20 countries belonging to different income levels. Overall, the results highlight that higher foreign direct investment inflows surge carbon dioxide emissions whereby the pollution haven hypothesis is evidenced to hold for the G-20 nations of concern. Similarly, both at the aggregated and disaggregated levels, greater consumption of energy is witnessed to boost carbon dioxide emissions in the long run. Moreover, urbanization is found to trigger carbon dioxide emissions for the G-20 nations overall and the lower-middle-income G-20 nations. Further, the causality analysis reveals that carbon dioxide emissions have bidirectional causal relationships with foreign direct investment inflows, energy consumption, and urbanization. In line with these major findings, this study recommends that the governments of the G-20 countries inhibit inflows of dirty foreign direct investments, reduce fossil fuel dependency, and adopt green urbanization policies for achieving higher economic growth without marginalizing environmental well-being.

Potential economic indicators and environmental quality in African economies: new insight from cross-sectional autoregressive distributed lag approach.

Growing economic development and substantial demographic shifts may have a momentous consequence on environmental quality in a number of African countries. Consequently, this recent study offers the opportunity to explore the nexus among unobserved influential economic indicators and environmental quality (measured through CO2 emissions) in a panel of 26 African economies spanning from 1990 to 2018. The aggregated panel is sub-classified into net exporters (NEC) and net importers (NIC) of embodied carbon. Considering existence of cross-section reliance and heterogeneity issues, all observed series are preliminarily confirmed stationary and cointegrated. Further, key outcomes from the common correlated effect Pooled Mean Group (CCEPMG) estimator through cross-sectional autoregressive distributed lag (CSARDL) approach showed that (i) economic growth and fossil fuel energy use stimulate environmental degradation among all panels, (ii) urbanization and trade openness enhance environmental quality in NEC panel while environmental damage is increased in NIC and aggregated panels, (iii) financial development also enhanced environmental quality in the totaled and NEC panel of African countries, but rather maturated climate deterioration in NIC panel, (iv) industrialization had a substantial adverse effect on environmental quality through surge in emission of CO2 concerning the aggregated panel and NEC African states, and (v) overall the environmental Kuznets curve (EKC) conjuncture is validated among all panels. The findings were also affirmed by Augmented Mean Group (AMG) technique. Finally, Dumitrescu-Hurlin Granger causality checks showed strong causal affiliations heterogeneously across all panels. From the policy perspective, the analytical outcomes from this study summarily encourage the introduction of profitable policies that can facilitate green energy and economic structural change to diminish the degree of environmental degradation from emission of CO2. Steps to strengthen a low-carbon and sustainable green environment should therefore collectively address these factors during policy growth.

Estimation of the return periods of maxima rainfall and floods at the Pra River Catchment, Ghana, West Africa using the Gumbel extreme value theory.

The Pra river catchment in Ghana is adversely affected by perennial flooding from high-intensity rainfall events. To aid in flood management at the catchment, the Gumbel extreme value distribution has been used to estimate the return periods of maxima rainfall, flood, and consecutive dry and wet days (CDD and CWD) for a period of 5 to 100 years. The results revealed an expected increase in maxima rainfall, CDD and CWD. Maxima rainfall favours the south of the catchment while the CDD decreases northward. Furthermore, an increase in the magnitude of CWD observed at the centre of the catchment had a maximum of approximately 30 days for the 100 year return period, while lower flood volumes had a higher recurrence of 50% to 100% for 1 to 2 year return periods. The inclusion of a projected increase in anthropogenic activities and climate factors at the catchment will slightly affect the magnitude of these variables for the various return periods. Nonetheless, the findings in this study will be of essential input to policy implementation of the Integrated Water Resource Management Plan for river catchments in Ghana, West Africa.

Investigation on key contributors of energy consumption in dynamic heterogeneous panel data (DHPD) model for African countries: fresh evidence from dynamic common correlated effect (DCCE) approach.

The main aim of this current study is to empirically scrutinize the determinants of energy consumption for 24 African countries sub-grouped into three panels based on income levels: low-, lower-middle-, and upper-middle-income countries, from 1990 to 2015. Due to the presence of heterogeneity and cross-sectional reliance among country groups, recently developed econometric approaches, which include cross-sectional Im, Pesaran, and Shin together with cross-sectional Augmented Dickey-Fuller stationarity tests, Pedroni and Westerlund-Edgerton cointegration assessment, dynamic common correlated effect estimation approach and Dumitrescu-Hurlin Granger causality test are employed. Empirically, our findings depict analyzed variables are stationary and characterized by long-term stability affiliations for all panels. Economic growth, urbanization, population growth, and oil price with labor and capital stock as intermittent variables had palpable significant positive sway on energy consumption for all panels though their respective weight of contribution differed from one country group to another. The granger test of causation unveiled that (i) among all panels, urbanization and energy consumption are connected bidirectionally, whereas population growth causes energy consumption; (ii) a one-way causal link from economic growth to energy use is evidenced in low-income African countries, whereas a two-sided connection is confirmed in both lower-middle- and upper-middle-income economies; (iii) a bilateral causal association in low-income African nations is observed amid oil price and energy use, while a uni-lateral relationship extends from oil price to energy consumption in both lower-middle- and upper-middle-income nations in Africa. Such new methodologies and findings reveal that the long-term estimated effects as well as causal affiliations amid variables are skewed by different income levels of African countries in an attempt to conserve energy. Policy recommendations are further propose.

Analysis of Haematological Parameters as Predictors of Malaria Infection Using a Logistic Regression Model: A Case Study of a Hospital in the Ashanti Region of Ghana.

Malaria is the leading cause of morbidity in Ghana representing 40-60% of outpatient hospital attendance with about 10% ending up on admission. Microscopic examination of peripheral blood film remains the most preferred and reliable method for malaria diagnosis worldwide. But the level of skills required for microscopic examination of peripheral blood film is often lacking in Ghana. This study looked at determining the extent to which haematological parameters and demographic characteristics of patients could be used to predict malaria infection using logistic regression. The overall prevalence of malaria in the study area was determined to be 25.96%; nonetheless, 45.30% of children between the ages of 5 and 14 tested positive. The binary logistic model developed for this study identified age, haemoglobin, platelet, and lymphocyte as the most significant predictors. The sensitivity and specificity of the model were 77.4% and 75.7%, respectively, with a PPV and NPV of 52.72% and 90.51%, respectively. Similar to RDT this logistic model when used will reduce the waiting time and improve the diagnosis of malaria.