• Energy Research
• 2021-2025close
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AbstractBackgroundBinge alcohol exposure during adolescence results in long‐lasting alterations in the brain and behavior. For example, adolescent intermittent ethanol (AIE) exposure in rodents results in long‐term loss of functional connectivity among prefrontal cortex (PFC) and striatal regions as well as a variety of neurochemical, molecular, and epigenetic alterations. Interneurons in the PFC and striatum play critical roles in behavioral flexibility and functional connectivity. For example, parvalbumin (PV) interneurons are known to contribute to neural synchrony and cholinergic interneurons contribute to strategy selection. Furthermore, extracellular perineuronal nets (PNNs) that surround some interneurons, particularly PV+ interneurons, further regulate cellular plasticity. The effect of AIE exposure on the expression of these markers within the PFC is not well understood.MethodsThe present study tested the hypothesis that AIE exposure reduces the expression of PV+ and choline acetyltransferase (ChAT)+ interneurons in the adult PFC and striatum and increases the related expression of PNNs (marked by binding of Wisteria floribunda agglutinin lectin) in adulthood. Male rats were exposed to AIE (5 g/kg/day, 2‐days‐on/2‐days‐off, i.e., P25 to P54) or water (CON), and brain tissue was harvested in adulthood (>P80). Immunohistochemistry and co‐immunofluorescence were used to assess the expression of ChAT, PV, and PNNs within the adult PFC and striatum following AIE exposure.ResultsChAT and PV interneuron densities in the striatum and PFC were unchanged after AIE exposure. However, PNN density in the PFC of AIE‐exposed rats was greater than in CON rats. Moreover, significantly more PV neurons were surrounded by PNNs in AIE‐exposed subjects than controls in both PFC subregions assessed: orbitofrontal cortex (CON = 34%; AIE = 40%) and medial PFC (CON = 10%; AIE = 14%).ConclusionsThese findings indicate that, following AIE exposure, PV interneuron expression in the adult PFC and striatum is unaltered, while PNNs surrounding these neurons are increased. This increase in PNNs may restrict the plasticity of the ensheathed neurons, thereby contributing to impaired microcircuitry in frontostriatal connectivity and related behavioral impairments.

Wind energy is an important renewable energy source for generating electricity that has the potential to replace fossil fuels. Herein, we propose confidence intervals for the difference between the coefficients of variation of Weibull distributions constructed using the concepts of the generalized confidence interval (GCI), Bayesian methods, the method of variance estimates recovery (MOVER) based on Hendricks and Robey’s confidence interval, a percentile bootstrap method, and a bootstrap method with standard errors. To analyze their performances, their coverage probabilities and expected lengths were evaluated via Monte Carlo simulation. The simulation results indicate that the coverage probabilities of GCI were greater than or sometimes close to the nominal confidence level. However, when the Weibull shape parameter was small, the Bayesian- highest posterior density interval was preferable. All of the proposed confidence intervals were applied to wind speed data measured at 90-meter wind energy potential stations at various regions in Thailand.

Novel N-methyl-4-piperidinol (MPDL)-based solvents have been considered as high potential solvents for post combustion carbon capture, especially for power generation industry. To comprehensively investigate the CO2 absorption-regeneration performance of MPDL-based solvents, transport properties (i.e., density, viscosity, and physical CO2 diffusivity) are required. These data are reported in the literature and can be estimated by conventional predictive correlations. However, the conventional correlation is applicable for an individual solvent at various blended ratios and temperatures. Thus, artificial neural network (ANN) was then applied for prediction of the transport properties of MPDL-based solvents, including aqueous solutions of MPDL, MPDL-monoethanolamine (MEA), MPDL-2-amino-2-methyl-1-propanol (AMP), and MPDL-piperazine (PZ). Three learning algorithms of (i) Levenberg–Marquardt (LM), (ii) Bayesian Regularization (BR), and (iii) Scaled Conjugate Gradient (SCG) were applied to develop the predictive ANN models with various hidden neurons. As a result, 6 hidden neurons BR-ANN model was the most convincible single prediction platform for the three transport properties. The develop model can be very beneficial for further applications associated with the novel MPDL-based solvents.

To mitigate climate change, individual greenhouse gas emissions need to decline substantially. This paper empirically explores the relationship between individual carbon footprints and carbon literacy as well as socio-economic and attitudinal factors. To operationalize carbon literacy, we distinguish between carbon knowledge and carbon engagement. Our econometric analysis uses widely representative survey data for 1000 individuals in Germany and distinguishes between components of an aggregate carbon footprint and of carbon footprints related with electricity consumption, heating, motorized individual transport, aviation, and dietary choices. We find a negative and sizeable correlation between carbon engagement and the aggregate footprint, as well as the footprints related to electricity consumption and diet. For example, a one-unit increase in our index reflecting carbon engagement corresponds to a decrease in the aggregate carbon footprint of about 4%. Furthermore, for carbon knowledge we find a negative correlation with the carbon footprint from diet. We also find significant correlations between the carbon footprints and gender, age, income, education, environmental preferences, and policy orientation, which generally exhibit the intuitively expected signs, but differ somewhat across activities. Overall, our findings support the notion that fostering carbon engagement represents a more effective strategy for reducing individuals' carbon footprints than enhancing carbon knowledge.

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

Multiple reservoir operation is of paramount importance due to tradeoffs in water supply and their cost functions. Understanding this complexity is important for optimizing water supply and increasing synergies gained from the joint operation. Therefore, this study aimed to develop a conceptual framework for addressing the effects of climate change on water security under the operating rules of the multiple reservoir system in northern France. A dynamic programming approach (DP) was employed to find the cost–benefit analysis that best fit with the objectives of reservoir operation, while the space rule was applied to balance the available space in each reservoir of a parallel system. A finite-horizon optimal regulation was then adopted for determining daily reservoir storage based on probability-based rule curves. The results indicated that the predicted inflow during the drawdown–refill cycle period to the Marne and Pannecière reservoirs would be the largest and lowest, respectively. The proposed upper rule curves during high-flow conditions suggested that the release from Aube reservoir should be postponed from July to August until September. At 50- and 100-year return periods, quite a high release rate from Seine and Marne reservoirs was observed during the dry season. A decrease in future water supply from Pannecière reservoir was found during summer, while the withdrawal in November could cause excessive water in the Seine tributary and Paris City. Under low-flow conditions in all return periods, the proposed lower rule curves recommended that the reservoir storage should go below the current operating rule, with a clear difference in July (the largest in Marne and the smallest in Pannecière) and almost no difference in November. Moreover, the web-based support system IRMaRA was developed for revising operating rules of four main reservoirs located in the Seine River Basin. The novelty of this modeling framework would contribute to the practice of deriving optimal operating rules for a multi-reservoir system by the probability-based rule curve method. Based on the evaluation of the effects of applying the estimated reservoir storage capacity under different return periods, both less overflow and water shortage represented by different levels of quantity and severity can be expected compared to the existing target storage at specified control points. Finally, the obtained finding revealed that the application of dynamic programming for reservoir optimization would help in developing a robust operating policy for tackling the effects of climate change.