Using time-series data of Pakistan from 1990 to 2019, this study explores the asymmetric effects of political instability on clean energy consumption and CO2 emissions. The results from the traditional ARDL model show that political stability lessens environmental damage by reducing CO2 emissions in the long run. However, when we used the nonlinear ARDL approach we found that political instability reduces the consumption of clean energy but also leads to damage environmental quality in long run in Pakistan. While political stability increases the consumption of clean energy but also helps to improve environmental quality in the short run in Pakistan. Thus, macroeconomic policies to promote expansion in clean energy consumption will directly stimulate green economic growth and environmental quality.

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The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting.

Using time-series data of Pakistan from 1990 to 2019, this study explores the asymmetric effects of political instability on clean energy consumption and CO2 emissions. The results from the traditional ARDL model show that political stability lessens environmental damage by reducing CO2 emissions in the long run. However, when we used the nonlinear ARDL approach we found that political instability reduces the consumption of clean energy but also leads to damage environmental quality in long run in Pakistan. While political stability increases the consumption of clean energy but also helps to improve environmental quality in the short run in Pakistan. Thus, macroeconomic policies to promote expansion in clean energy consumption will directly stimulate green economic growth and environmental quality.

A 33-year male with history of penetrating trauma to left upper chest in 2006, presented through Medical unit to Radiology Department with complain of hemoptysis. Chest X-ray showed a soft tissue lesion in left upper lobe with a linear metallic foreign body. Contrast enhanced CT scan of chest and later CTA was performed which showed a saccular aneurysm arising from mediastinal part of left common aortic artery surrounded by thrombosis with a cylindrical linear metallic foreign body. He was planned for endovascular repair with stenting which he could not afford due to financial constraints. He is currently on conservative follow up. Vascular lesions can be serious complications resulting from blunt or penetrating trauma, when presenting with hemoptysis due to pseudaoneurysms formation even after so many years of trauma.

The current study aims to examine poverty theories in light of new perspectives and approaches. The methodology used for this research, that is solely qualitative, is based on the literature. The study explores a variety of theories on poverty, including classical, neoclassical, Keynesian, Marxist/radical, monetary, cultural, ecological, economic, political and the theory of social exclusion and social capital. Some policy measures are suggested to overcome poverty such as economic growth must benefit all citizens through progressive tax systems, education, health, and work opportunities, improving infrastructure and local industries competitiveness, increased transparency, stricter enforcement, and strengthening of institutions to prevent corruption, encourage political participation through voter education, parties, and debate, encourage trade, investment, renewable energy sources, and self-help strategies to reduce poverty.

The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method of data handling neural network (GMDH-NN) algorithm. The data used in this study is spanning from April 14, 2017, to October 30, 2020, covering 1296 observations. We predict the volatility of four cryptocurrencies, namely Bitcoin, Ethereum, XRP, and Tether, and compare their predictive power in terms of forecasting accuracy. The predictive capabilities of CSS, NNETAR, and GMDH-NN are compared and evaluated by mean absolute error (MAE) and root-mean-square error (RMSE). Regarding the return volatility of Bitcoin and XRP markets, the forecasted results remarkably suggest that in contrast to rival approaches, the CSS can be an effective model to boost the predicting accuracy in the sense that it has the lowest forecast errors. Considering the Ethereum markets’ volatility, the MAE and RMSE associated with NNETAR are smaller than the MAE and RMSE of CSS and GMDH-NN algorithm, which ensures the effectiveness of NNETAR as compared to competing approaches. Similarly, in case of Tether markets’ volatility, the corresponding MAE and RMSE reveal that the GMDH-NN algorithm is an efficient technique to enhance the forecasting performance. We notice that no single tool performed uniformly for all cryptocurrency markets. The policymakers can adopt the model for forecasting cryptocurrency volatility accordingly.