Pakistan’s agricultural sector growth is dwindling from the last several years due to insufficient foreign direct investment (FDI) and a drastic climate change-induced raise in temperature, which are severely affecting agricultural production. The FDI has paramount importance for the economy of developing countries as well as the improvement of agricultural production. Based on the time series data from 1984 to 2017, this paper aims to highlight the present situation of the agriculture sector of Pakistan and empirically analyze the short-run and long-run impact of Chinese foreign direct investment (CFDI), climate change, and CO2 emissions on agricultural productivity and causality among the variables. The Autoregressive Distributed Lag Model (ARDL) model and Granger Causality test were employed to find out the long-run, short-run, and causal relationships among the variables of interest. Furthermore, we have employed the Error Correction Model (ECM) to know the convergence of the equilibrium path. The bound test results verified the existence of a long-run association, and the empirical findings confirmed that Chinese FDI has a significant and positive impact, while climate change and CO2 emissions has negative impact on the agricultural growth of Pakistan both in the short-run and long-run. Granger Causality test results revealed that variables of interest exhibit bi-directional and uni-directional causality. The sector-wise flow of FDI reveals that the agriculture sector of Pakistan has comparatively received a less amount of FDI than other sectors of the economy. Based on the findings, it was suggested to the Government of Pakistan and policymakers to induce more FDI in the agriculture sector. Such policies would be helpful for the progress of the agriculture sector as well as for the economic growth of Pakistan.

Background: Obstructive sleep apnea (OSA) is a chronic sleeping disorder. The analysis of pharynx and its surrounding tissues can play a vital role in understanding the pathogenesis of OSA. Classification of pharynx is a crucial step in the analysis of OSA. Objective: An automatic pharyngeal classification from magnetic resonance images (MRI) and the influence of different features can help in analyzing the pharynx anatomy. However, the state-of-the-art classifiers do not provide any insight regarding the features’ selection and their influence. Methods: A visual analysis-based classifier is developed to classify the pharynx from MRI datasets. The classification pipeline consists of different stages including pre-processing to select the initial candidates, extraction of categorical and numerical features to form a multidimensional features space, and a supervised classifier trained by using visual analytics and silhouette coefficient to classify the pharynx. Results: The pharynx is classified automatically and gives an approximately 86% Jaccard coefficient by evaluating the classifier on different MRI datasets. The expert’s knowledge can be utilized to select the optimal features and their corresponding weights during the training phase of the classifier. Conclusion: The proposed classifier is accurate and more efficient in terms of computational cost. It provides additional insight to better understand the influence of different features individually and collectively. It finds its applications in epidemiological studies where large datasets need to be analyzed.

The purpose of this research is to explore the association among energy, environment and economic growth in Latin-American countries from 1990-2014 by using multivariate Structure. This study used number of co-integration techniques to confirm log run relationship among environment, and energy. The study findings also show the effect of the energy on environment in the long run by using FMOLS and DOLS. In addition, this research also employed the causality test to study the causal relation among the variables. The outcomes of the various tests of co-integration endorse a longrun relationship among renewable energy (REN) and non-renewable (NREN) consumption and environment. The long run results show that the use of renewable energy source can reduce the CO 2 emissions in selected countries. Moreover, the non-renewable energy consumption is increasing CO 2 emissions. In addition, the direction of the causality is unidirectional from REN to CO 2 , NREN to CO 2 and GDP to CO 2 . However, there is absence of two-way causality among the variables in the model.

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