• Energy Research
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Achieving environmental sustainability has become a global concern amidst increasing climate change threat. Using quarterly frequency data for the case of Russia from 1992 to 2018, the present study explores the interaction between disaggregated energy consumption (renewable energy and non-renewable energy), trade flow and economic growth on a broader measure for environmental degradation (ecological footprint). The choice of the variables draws strength from initiative of the United Nations Sustainable Development Goals (UN-SDG, 7, 8 11 and 13) for responsible energy consumption and clean energy consumption while mitigating climate change issues. The study applied the quantile-on-quantile regression (QQR) and nonparametric causality-in-quantiles to capture these associations. The outcomes from the QQR disclosed that in the majority of the quantiles, trade openness and renewable energy use contribute to environmental sustainability, while nonrenewable energy amplifies ecological footprint. Furthermore, growth in Russia escalates its ecological footprint. Moreover, in the majority of the quantiles, all the exogenous variables can predict ecological footprint. Given the outcomes of this study, it outlines the need for a paradigm shift for alternative and clean energy consumption in Russian energy mix amidst its economic growth trajectory while accounting for green-development approaches. Pathways to fully achieve the sustainability targets are carefully outlined in the concluding section.

Achieving environmental sustainability has become a global concern amidst increasing climate change threat. Using quarterly frequency data for the case of Russia from 1992 to 2018, the present study explores the interaction between disaggregated energy consumption (renewable energy and non-renewable energy), trade flow and economic growth on a broader measure for environmental degradation (ecological footprint). The choice of the variables draws strength from initiative of the United Nations Sustainable Development Goals (UN-SDG, 7, 8 11 and 13) for responsible energy consumption and clean energy consumption while mitigating climate change issues. The study applied the quantile-on-quantile regression (QQR) and nonparametric causality-in-quantiles to capture these associations. The outcomes from the QQR disclosed that in the majority of the quantiles, trade openness and renewable energy use contribute to environmental sustainability, while nonrenewable energy amplifies ecological footprint. Furthermore, growth in Russia escalates its ecological footprint. Moreover, in the majority of the quantiles, all the exogenous variables can predict ecological footprint. Given the outcomes of this study, it outlines the need for a paradigm shift for alternative and clean energy consumption in Russian energy mix amidst its economic growth trajectory while accounting for green-development approaches. Pathways to fully achieve the sustainability targets are carefully outlined in the concluding section.

Solar thermal collectors convert sunlight into useful thermal energy by absorbing its incoming radiation. Concentrated solar power technologies use the parabolic trough solar collector to collect solar energy with temperatures ranging from 325– 700 K. The tangent hyperbolic fluid model is one of the most important non-Newtonian fluid models. Laboratory studies demonstrate that this model accurately predicts the shear thinning phenomenon. In addition, tangent hyperbolic fluid has a better heat transfer performance due to its rheological bearing at various shear rates. The current study investigates the heat transmission performance of Darcy–Forchheimer tangent hyperbolic radiative inclined cylindrical film movement in parabolic trough solar collector with an irregular heat sink/source utilizing the Levenberg–Marquardt technique and backpropagated neural networks. Through the implementation of required transformations, this system is turned into an equivalent nonlinear ordinary differential system. The findings are investigated for Newtonian and tangent hyperbolic fluid cases to understand the rheological characteristics. The outcomes are considered using graphical and mathematical evaluations. Fluids featuring tangent hyperbolic rheological conductivity are obligatory for active rate of heat diffusion. As a consequence, these fluids may be employed in Parabolic Trough Solar Collector for increased heat transmission rate and operational usage of solar energy. Furthermore, We create a dataset using the Runge–Kutta fourth-order shooting technique to create the proposed multilayer perceptron artificial neural network. The data points representing the MoD values are observed to be closely clustered around the zero deviation line. Additionally, it is important to highlight that these data points have relatively small numerical values. Moreover, when calculating the average MoD values for each output, it becomes evident that they are consistently very low.

Solar thermal collectors convert sunlight into useful thermal energy by absorbing its incoming radiation. Concentrated solar power technologies use the parabolic trough solar collector to collect solar energy with temperatures ranging from 325– 700 K. The tangent hyperbolic fluid model is one of the most important non-Newtonian fluid models. Laboratory studies demonstrate that this model accurately predicts the shear thinning phenomenon. In addition, tangent hyperbolic fluid has a better heat transfer performance due to its rheological bearing at various shear rates. The current study investigates the heat transmission performance of Darcy–Forchheimer tangent hyperbolic radiative inclined cylindrical film movement in parabolic trough solar collector with an irregular heat sink/source utilizing the Levenberg–Marquardt technique and backpropagated neural networks. Through the implementation of required transformations, this system is turned into an equivalent nonlinear ordinary differential system. The findings are investigated for Newtonian and tangent hyperbolic fluid cases to understand the rheological characteristics. The outcomes are considered using graphical and mathematical evaluations. Fluids featuring tangent hyperbolic rheological conductivity are obligatory for active rate of heat diffusion. As a consequence, these fluids may be employed in Parabolic Trough Solar Collector for increased heat transmission rate and operational usage of solar energy. Furthermore, We create a dataset using the Runge–Kutta fourth-order shooting technique to create the proposed multilayer perceptron artificial neural network. The data points representing the MoD values are observed to be closely clustered around the zero deviation line. Additionally, it is important to highlight that these data points have relatively small numerical values. Moreover, when calculating the average MoD values for each output, it becomes evident that they are consistently very low.

Chronic alcohol (ethanol) drinking changes central serotonin and dopamine levels, and thereby the functioning of brain circuits that support cognition and anxiety. Previously, it has been proven that Nigella sativa oil (NSO) improves cognition and reduces anxiety by regulating the neurotransmission but the underlying mechanisms are unknown.To address the knowledge gap, an in vivo experiment was done to investigate effects of NSO on behavior and neurotransmission in ethanol drinking Wistar male rats. Specifically, control, NSO treated, ethanol and ethanol + NSO treated groups were tested for changes in anxiety-like behavior, locomotor activity and learning and memory using the elevated plus-maze test (EPM) and light and dark (L&D) box test; open field test (OFT) and Morris water maze (MWM) test, respectively. Brain neurotransmitter concentrations were determined using HPLC-EC. To validate the in vivo findings, we assessed in silico the docking between NSO compounds and proteins using auto dock vina.Ethanol and NSO reduced weight in the ethanol and ethanol + NSO groups. Food intake, fluid consumption, calorie intake, and growth were similarly affected by ethanol and NSO. In the in behavioral tests, ethanol drinking rats spent less time in the open arms of the EPM and had fewer entries compared to controls, while ethanol + NSO group also showed reduced entries. Similar patterns were observed in the OFT. No differences were found in the L&D box test. In the memory tests, ethanol + NSO treatment increased latency in short-term memory, while ethanol consumption increased latency in retention. Neurochemical analysis revealed that ethanol + NSO treatment increased serotonin levels in the PFC and hippocampus while reducing dopamine levels in the PFC compared to all groups, and in the hippocampus compared to control and NSO groups. The in silico experiment revealed that NSO has nine main active compounds. By molecular docking, we found that all nine compounds showed good binding affinity with our target proteins but the best docking values were obtained with thymoquinone and dithymoquinone. The binding affinity estimations identified the superior binding affinity and efficiency of dithymoquinone over all nine NSO compounds for serotonin, dopamine receptors and MAO-enzymes.NSO partially modulated ethanol induced neurobehavioral and neurochemical alterations, improving serotonin levels but not fully reversing behavioral deficits. Further studies are needed to explore its protective potential.

Chronic alcohol (ethanol) drinking changes central serotonin and dopamine levels, and thereby the functioning of brain circuits that support cognition and anxiety. Previously, it has been proven that Nigella sativa oil (NSO) improves cognition and reduces anxiety by regulating the neurotransmission but the underlying mechanisms are unknown.To address the knowledge gap, an in vivo experiment was done to investigate effects of NSO on behavior and neurotransmission in ethanol drinking Wistar male rats. Specifically, control, NSO treated, ethanol and ethanol + NSO treated groups were tested for changes in anxiety-like behavior, locomotor activity and learning and memory using the elevated plus-maze test (EPM) and light and dark (L&D) box test; open field test (OFT) and Morris water maze (MWM) test, respectively. Brain neurotransmitter concentrations were determined using HPLC-EC. To validate the in vivo findings, we assessed in silico the docking between NSO compounds and proteins using auto dock vina.Ethanol and NSO reduced weight in the ethanol and ethanol + NSO groups. Food intake, fluid consumption, calorie intake, and growth were similarly affected by ethanol and NSO. In the in behavioral tests, ethanol drinking rats spent less time in the open arms of the EPM and had fewer entries compared to controls, while ethanol + NSO group also showed reduced entries. Similar patterns were observed in the OFT. No differences were found in the L&D box test. In the memory tests, ethanol + NSO treatment increased latency in short-term memory, while ethanol consumption increased latency in retention. Neurochemical analysis revealed that ethanol + NSO treatment increased serotonin levels in the PFC and hippocampus while reducing dopamine levels in the PFC compared to all groups, and in the hippocampus compared to control and NSO groups. The in silico experiment revealed that NSO has nine main active compounds. By molecular docking, we found that all nine compounds showed good binding affinity with our target proteins but the best docking values were obtained with thymoquinone and dithymoquinone. The binding affinity estimations identified the superior binding affinity and efficiency of dithymoquinone over all nine NSO compounds for serotonin, dopamine receptors and MAO-enzymes.NSO partially modulated ethanol induced neurobehavioral and neurochemical alterations, improving serotonin levels but not fully reversing behavioral deficits. Further studies are needed to explore its protective potential.

Abstract Renewable energy and green innovation can enhance environmental performance by encouraging international trade, as anticipated by the current theoretical framework. This study investigates how renewable energy and eco-innovation affect international trade and environmental performance using quarterly data series from 1981 to 2018 for China. The study applies a quantile-based autoregressive distributed lag model. The results indicate that environmental innovation and renewable energy are consistent with environmental quality, while trade openness and urbanization contribute to environmental degradation by stimulating CO2 emissions. In addition, the quantile causality test showed a bidirectional causality between renewable energy and CO2 emissions, trade openness and CO2 emissions, and urbanization and CO2 emissions. Additionally, the findings lend theoretical support for the formulation of environmentally protective policies to better understand the role of renewable energy in stimulating international trade, which eventually enhances environmental performance. Integr Environ Assess Manag 2022;18:813–823. © 2021 SETAC KEY POINTS The study applies the Quantile based Autoregressive Distributed Lag Model. The results indicate that eco-innovation and renewable energy are consistent with environmental quality. Trade openness and urbanization contribute to environmental degradation.

Abstract Renewable energy and green innovation can enhance environmental performance by encouraging international trade, as anticipated by the current theoretical framework. This study investigates how renewable energy and eco-innovation affect international trade and environmental performance using quarterly data series from 1981 to 2018 for China. The study applies a quantile-based autoregressive distributed lag model. The results indicate that environmental innovation and renewable energy are consistent with environmental quality, while trade openness and urbanization contribute to environmental degradation by stimulating CO2 emissions. In addition, the quantile causality test showed a bidirectional causality between renewable energy and CO2 emissions, trade openness and CO2 emissions, and urbanization and CO2 emissions. Additionally, the findings lend theoretical support for the formulation of environmentally protective policies to better understand the role of renewable energy in stimulating international trade, which eventually enhances environmental performance. Integr Environ Assess Manag 2022;18:813–823. © 2021 SETAC KEY POINTS The study applies the Quantile based Autoregressive Distributed Lag Model. The results indicate that eco-innovation and renewable energy are consistent with environmental quality. Trade openness and urbanization contribute to environmental degradation.