Assessing the decarbonization of electricity generation in major emitting countries by 2030 and 2050: Transition to a high share renewable energy mix.

The urgent need to mitigate the severe environmental impacts of climate change necessitates a transition to a low-carbon energy infrastructure, crucial for decarbonization and achieving global sustainability goals. This study investigates the decarbonization trajectories of five major economies and significant carbon emitters: the United States of America (USA), China, Japan, Germany, and India. We focus on evaluating two decarbonization scenarios for power generation. Scenario 1 explores the use of a generic storage system for reducing critical excess electricity production (CEEP), maintaining the same thermal power plant capacity as in the reference year 2021. In contrast, Scenario 2 models thermal power plants to meet the exact electricity demand without introducing a new electricity storage system. The primary aim is to assess the feasibility and implications of achieving a 100% share of renewable and nuclear energy by 2030 and 2050 in these countries. EnergyPLAN software was utilized to model and simulate the electricity systems of these countries. The two scenarios represent different degrees of renewable energy integration, demonstrating possible transitional pathways towards an environmentally friendly electricity generation system. The study provides a comparative analysis of the outcomes for each country, focusing on carbon emissions reduction and the impact on annual total costs in 2030 and 2050. Results show that by 2030, China could reduce its emissions by 88.5% and 85.14% in Scenarios 1 and 2, relative to 2021 levels. From the two scenarios considered in all the countries, India records the highest percentage reduction while Germany has the least percentage emission in reference to 2021, with a potential decrease of 90.63% and 52.42% respectively. By 2050, carbon emissions in the USA will be reduced by 83% and 79.8% using Scenario 1 and Scenario 2 decarbonization pathways. This research significantly contributes to understanding the decarbonization potential of global electricity generation. It provides vital data for policymakers, energy planners, and stakeholders involved in developing sustainable energy policies.

Prioritizing Non-Carbon Dioxide Removal Mitigation Strategies Could Reduce the Negative Impacts Associated with Large-Scale Reliance on Negative Emissions.

Carbon dioxide removal (CDR) is necessary for reaching net zero emissions, with studies showing potential deployment at multi-GtCO2 scale by 2050. However, excessive reliance on future CDR entails serious risks, including delayed emissions cuts, lock-in of fossil infrastructure, and threats to sustainability from increased resource competition. This study highlights an alternative pathway─prioritizing near-term non-CDR mitigation and minimizing CDR dependence. We impose a 1 GtCO2 limit on global novel CDR deployment by 2050, forcing aggressive early emissions reductions compared to 8-22 GtCO2 in higher CDR scenarios. Our results reveal that this low CDR pathway significantly decreases fossil fuel use, greenhouse gas (GHG) emissions, and air pollutants compared to higher CDR pathways. Driving rapid energy transitions eases pressures on land (including food cropland), water, and fertilizer resources required for energy and negative emissions. However, these sustainability gains come with higher mitigation costs from greater near-term low/zero-carbon technology deployment for decarbonization. Overall, this work provides strong evidence for maximizing non-CDR strategies such as renewables, electrification, carbon neutral/negative fuels, and efficiency now rather than betting on uncertain future CDR scaling. Ambitious near-term mitigation in this decade is essential to prevent lock-in and offer the best chance of successful deep decarbonization. Our constrained CDR scenario offers a robust pathway to achieving net zero emissions with limited sustainability impacts.

Juxtaposing Sub-Sahara Africa's energy poverty and renewable energy potential.

Recently, the International Energy Agency (IEA) released a comprehensive roadmap for the global energy sector to achieve net-zero emission by 2050. Considering the sizeable share of (Sub-Sahara) Africa in the global population, the attainment of global energy sector net-zero emission is practically impossible without a commitment from African countries. Therefore, it is important to study and analyze feasible/sustainable ways to solve the energy/electricity poverty in Africa. In this paper, the energy poverty in Africa and the high renewable energy (RE) potential are reviewed. Beyond this, the generation of electricity from the abundant RE potential in this region is analyzed in hourly timestep. This study is novel as it proposes a Sub-Sahara Africa (SSA) central grid as one of the fastest/feasible solutions to the energy poverty problem in this region. The integration of a sizeable share of electric vehicles with the proposed central grid is also analyzed. This study aims to determine the RE electricity generation capacities, economic costs, and supply strategies required to balance the projected future electricity demand in SSA. The analysis presented in this study is done considering 2030 and 2040 as the targeted years of implementation. EnergyPLAN simulation program is used to simulate/analyze the generation of electricity for the central grid. The review of the energy poverty in SSA showed that the electricity access of all the countries in this region is less than 100%. The analysis of the proposed central RE grid system is a viable and sustainable option, however, it requires strategic financial planning for its implementation. The cheapest investment cost from all the case scenarios in this study is $298 billion. Considering the use of a single RE technology, wind power systems implementation by 2030 and 2040 are the most feasible options as they have the least economic costs. Overall, the integration of the existing/fossil-fueled power systems with RE technologies for the proposed central grid will be the cheapest/easiest pathway as it requires the least economic costs. While this does not require the integration of storage systems, it will help the SSA countries reduce their electricity sector carbon emission by 56.6% and 61.8% by 2030 and 2040 respectively.

Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images.

COVID-19 is a contagious disease that affects the human respiratory system. Infected individuals may develop serious illnesses, and complications may result in death. Using medical images to detect COVID-19 from essentially identical thoracic anomalies is challenging because it is time-consuming, laborious, and prone to human error. This study proposes an end-to-end deep-learning framework based on deep feature concatenation and a Multi-head Self-attention network. Feature concatenation involves fine-tuning the pre-trained backbone models of DenseNet, VGG-16, and InceptionV3, which are trained on a large-scale ImageNet, whereas a Multi-head Self-attention network is adopted for performance gain. End-to-end training and evaluation procedures are conducted using the COVID-19_Radiography_Dataset for binary and multi-classification scenarios. The proposed model achieved overall accuracies (96.33% and 98.67%) and F1_scores (92.68% and 98.67%) for multi and binary classification scenarios, respectively. In addition, this study highlights the difference in accuracy (98.0% vs. 96.33%) and F_1 score (97.34% vs. 95.10%) when compared with feature concatenation against the highest individual model performance. Furthermore, a virtual representation of the saliency maps of the employed attention mechanism focusing on the abnormal regions is presented using explainable artificial intelligence (XAI) technology. The proposed framework provided better COVID-19 prediction results outperforming other recent deep learning models using the same dataset.

Sustainability energy security: 20 years assessment of the West African Nations using a comprehensive entropy-TOPSIS analysis.

Energy security in Africa has become a crucial issue in recent times due to the imminent lack of access to electricity, increasing energy demand informed by economic growth, population growth, and forecasts that point to business as usual on the continent based on these variables. While the West African region has abundant energy resources, they have not yet been translated into sustainable energy security, as it pertains to energy availability. This is a persistent challenge that needs to be addressed to support economic growth and social development in the region. Therefore, this study aims to assess the sustainable energy security in five West African countries (Nigeria, Senegal, Ghana, Cote d'Ivoire, and Togo), using nine energy security indicators and taking into cognizance, the energy, economic, social, and environmental security dimensions. The entropy-TOPSIS methodology of multi-criteria decision-making (MCDM) is used in estimating the energy security index across 20 years (2000-2019). The result showed that the situation in Cote d'Ivoire is reported to be "safe" in terms of sustainable energy security. It is reported that in Togo, energy security is at a "dangerous" level, which is ultimately tied to the low energy, economic, and societal security in the country. The findings of this study could be valuable for policymakers working on energy and climate policy at the national and regional levels. Based on the results, it may be necessary to take stronger legal action to ensure the implementation of energy security goals in the West African countries, which have struggled to meet their targets and have faced challenges in implementing policies at the desired pace.

Prediction of Cell Migration in MDA-MB 231 and MCF-7 Human Breast Cancer Cells Treated with Albizia Lebbeck Methanolic Extract Using Multilinear Regression and Artificial Intelligence-Based Models.

Breast cancer is a common cancer affecting women worldwide, and it progresses from breast tissue to other parts of the body through a process called metastasis. Albizia lebbeck is a valuable plant with medicinal properties due to some active biological macromolecules, and it's cultivated in subtropical and tropical regions of the world. This study reports the phytochemical compositions, the cytotoxic, anti-proliferative and anti-migratory potential of A. lebbeck methanolic (ALM) extract on strongly and weakly metastatic MDA-MB 231 and MCF-7 human breast cancer cells, respectively. Furthermore, we employed and compared an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multilinear regression analysis (MLR) to predict cell migration on the treated cancer cells with various concentrations of the extract using our experimental data. Lower concentrations of the ALM extract (10, 5 & 2.5 µg/mL) showed no significant effect. Higher concentrations (25, 50, 100 & 200 µg/mL) revealed a significant effect on the cytotoxicity and proliferation of the cells when compared with the untreated group (p < 0.05; n ≥ 3). Furthermore, the extract revealed a significant decrease in the motility index of the cells with increased extract concentrations (p < 0.05; n ≥ 3). The comparative study of the models observed that both the classical linear MLR and AI-based models could predict metastasis in MDA-MB 231 and MCF-7 cells. Overall, various ALM extract concentrations showed promising an-metastatic potential in both cells, with increased concentration and incubation period. The outcomes of MLR and AI-based models on our data revealed the best performance. They will provide future development in assessing the anti-migratory efficacies of medicinal plants in breast cancer metastasis.

Performance analysis and socio-enviro-economic feasibility study of a new hybrid energy system-based decarbonization approach for coal mine sites.

The mining sector contributes to 4-7 % of global GHG emissions, of which 1 % are from scope 1 and scope 2 emissions, caused by operations such as electricity consumption used for the mining process. China heavily relies on coal for power generation, and the energy demand for coal production in the country is primarily met by fossil-based electricity. In addition, the transportation of the mined coal to various destinations within the supply chain is achieved by fossil fuel-powered transport systems. These daily activities of the Chinese coal sector further compound foreign and domestic pressure on China to limit its carbon emissions. The current study attempts to provide a solution to the situation by investigating the feasibility of adopting renewable energy sources for the process of coal mining in Northern China. The selected coal mine is one out of 643 coal mines in Shanxi Province, with a combined production capacity of ∼1 billion tonnes of coal per annum. In addition, the excess electricity generated has been designated to produce hydrogen on-site as a refueling source for hydrogen fuelled-trucks to replace diesel fuelled-trucks in transporting coal. The analysis has been completed using HOMER Pro software, and the key contributions are summarized as follows. 4 different scenarios comprising of standalone solar photovoltaic, wind turbine, and diesel generator have been designed in the current study to serve a daily load of 215 MWh and 2.4 t of electricity for coal mining and hydrogen for transport of 100 % of the mined coal by road using hydrogen fuel cell trucks, respectively. A technical, economic, environmental, and social feasibility analysis have been investigated in the present work. A grid-tied system is subsequently added to the base scenario and the results are compared against the base system in an attempt to identify the more feasible option between the two systems. Also, a sensitivity analysis has been conducted to reveal the performance of the base system amidst future uncertainties. The findings in the current work could prove beneficial to China's quest to reach carbon peak by 2030 and achieve carbon neutrality by 2060.

Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction.

Solar energy technologies represent a viable alternative to fossil fuels for meeting increasing global energy demands. However, to increase the production of solar technologies in the global energy mix, the cost of production should be as competitive as other sources. This study focuses on the implementation of machine learning for estimating the thermophysical properties of nanofluids for nanofluid-based solar energy technologies as this would make the synthesis of nanofluids cost-effective. The prediction of thermal conductivity has gained a lot of research attention, whereas, the viscosity of nanofluids has less concentration of studies. The accurate prediction of the viscosity of hybrid nanofluids is important in estimating the heat transfer performance of nanofluids as regards their pump power requirements and convective heat transfer coefficient in several applications. The rigor of experimentations of hybrid nanofluids has necessitated the need for developing efficient and robust machine learning models for accurately estimating the viscosity of hybrid nanofluids for solar applications. Several studies were aimed at developing a predictive model for the viscosity of nanofluids; however, these models are limited to specific types of nanofluids. This study is aimed at developing a robust machine learning algorithm for predicting the viscosity of several hybrid nanofluids from reliable experimental data (700 datasets) culled from literature. This study implements a novel optimizable Gaussian process regression (O-GPR), which have not been previously used in this area, and compares the result with other commonly used machine learning algorithms like, Boosted tree regression (BTR), Artificial neural network (ANN), support vector regression (SVR), to accurately predict the viscosity of a wide range of Newtonian-based hybrid nanofluid. The input parameters used in training the machine learning models were temperature (T), volume fraction (VF), the acentric factor of the base fluid (ACF), nanoparticle size (NS), and nanoparticle density (ND). The prediction performance of the machine learning algorithms was tested using statistical metrics and was compared with theoretical models. The O-GPR model showed superior predictive performance with an R 2 of 0.999998 and an MSE of 0.0002552. The study conclusively states that the high accuracy prediction of thermophysical properties of nanofluid using robust machine learning models makes the design of nanofluid-based solar energy technologies more cost-effective.