Assessing the impact of human capital, renewable energy, population growth, economic growth, and climate change policies on achieving the sustainable development goals.

The global emphasis on achieving sustainable development goals necessitates the involvement of researchers and regulators worldwide. In light of this, recent research has examined the effect of human capital, renewable energy, population growth, economic growth, and environmental protection on the sustainable development goals (SDGs) in a developed economy like Pakistan, which is the most important country in the South Asian Association for Regional Cooperation (SAARC) region. This study analyzed secondary data from 1990 to 2019, using the World Development Indicators as the secondary data source. Using the augmented Dickey-Fuller test to investigate stationarity and the autoregressive distributed lag model to evaluate the nexus between variables, the researchers analyzed the relationship between the variables. The findings indicate that all predictors, such as the human capital index (HCI), renewable energy consumption, and renewable energy, exhibit a negative correlation with carbon emissions and a positive correlation with the SDGs. In this study, sustainability and the HCI are positively correlated. Reducing carbon emissions requires competent and dependable employees. As Pakistan transitions to renewable energy and strives for 30% green electricity by 2030, the report highlights the ecological benefits of controlled population growth. According to the Climate Change Performance Index (CCPI), effective climate policies advance the environmental objectives of a nation. Economic and population growth have a positive correlation with carbon emissions as well. These results facilitate Pakistani policymakers' creation of effective SDG-related initiatives for sustainable development.

GPU accelerated Cartesian GRAPPA reconstruction using CUDA.

BACKGROUND AND OBJECTIVE: GRAPPA (Generalized Auto-calibrating Partially Parallel Acquisition) is an advanced parallel MRI reconstruction method (pMRI) that enables under-sampled data acquisition with multiple receiver coils to reduce the MRI scan time and reconstructs artifact free image from the acquired under-sampled data. However, the reduction in MRI scan time comes at the expense of long reconstruction time. It is because the GRAPPA reconstruction time shows exponential growth with increasing number of receiver coils. Consequently, the conventional CPU platforms may not adhere to the requirements of fast data processing for MR image reconstruction. METHODS: Graphics Processing Units (GPUs) have recently emerged as a viable commodity hardware to reduce the reconstruction time of pMRI methods. This paper presents a novel GPU based implementation of GRAPPA using custom built CUDA kernels, to meet the rising demands of fast MRI processing. The proposed framework exploits intrinsic parallelism in the calibration and synthesis phases of GRAPPA reconstruction process, aiming to achieve high speed MR image reconstruction for various GRAPPA configuration settings using different number of receiver coils, auto-calibration signals (ACS), sizes of GRAPPA kernel and acceleration factors. In-vivo experiments (using 8, 12 and 30 receiver coils) are performed to compare the performance of the proposed GPU accelerated GRAPPA with the CPU based GRAPPA extensions and GPU counterpart. RESULTS: The results indicate that the proposed method achieves up to ≈47.8× , ≈17× and ≈3.8× speed up gains over multicore CPU (single thread), multicore CPU (8 thread) and Gadgetron (GPU based GRAPPA) respectively, without compromising the reconstruction accuracy. CONCLUSIONS: The proposed method reduces the GRAPPA reconstruction time by employing the calibration phase (GRAPPA weights estimation) and synthesis phase (interpolation) on GPU. Our study shows that the proposed GPU based parallel framework for GRAPPA reconstruction provides a solution for high-speed image reconstruction while maintaining the quality of the reconstructed images.