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Obesity has emerged as a major risk factor for human health, exacerbated by aging and changes in dietary habits. It represents a significant health challenge, particularly for older people. While numerous studies have examined the effects of obesity and aging on fat metabolism independently, research on their combined effects is limited. In the present study, the protective action against white fat accumulation after a high-fat diet (HFD) exerted by exogenous melatonin, a circadian hormone endowed with antioxidant properties also involved in fat metabolism, was investigated in a mouse model. For this purpose, a battery of tests was applied before and after the dietary and melatonin treatments of the animals, including epididymal white adipose tissue (eWAT) histological evaluations, transcriptomic and lipidomic analyses, real-time PCR tests, immunofluorescence staining, Western blot, the appraisal of serum melatonin levels, and transmission electron microscopy. This study found that aged mice on a high-fat diet (HFD) showed increased lipid deposition, inflammation, and reduced antioxidant glutathione (GSH) levels compared to younger mice. Lipidomic and transcriptomic analyses revealed elevated triglycerides, diglycerides, ceramides, and cholesterol, along with decreased sphingomyelin and fatty acids in eWAT. The genes linked to inflammation, NF-κB signaling, autophagy, and lipid metabolism, particularly the melatonin and glutathione pathways, were significantly altered. The aged HFD mice also exhibited reduced melatonin levels in serum and eWAT. Melatonin supplementation reduced lipid deposition, increased melatonin and GSH levels, and upregulated AANAT and MTNR1A expression in eWAT, suggesting that melatonin alleviates eWAT damage via the MTNR1A pathway. It also suppressed inflammatory markers (e.g., TNF-α, NLRP3, NF-κB, IL-1β, and CEBPB) and preserved mitochondrial function through enhanced mitophagy. This study highlights how aging and HFD affect lipid metabolism and gene expression, offering potential intervention strategies. These findings provide important insights into the mechanisms of fat deposition associated with aging and a high-fat diet, suggesting potential intervention strategies. [ABSTRACT FROM AUTHOR]

The in vitro maturation efficiency of porcine oocytes is relatively low, and this limits the production of in vitro porcine embryos. Since melatonin is involved in mammalian reproductive physiology, in this study, we have explored whether endogenously produced melatonin can help in porcine oocyte in vitro maturation. We have found, for the first time in the literature, that mitochondria are the major sites for melatonin biosynthesis in porcine oocytes. This mitochondrially originated melatonin reduces ROS production and increases the activity of the mitochondrial respiratory electron transport chain, mitochondrial biogenesis, mitochondrial membrane potential, and ATP production. Therefore, melatonin improves the quality of oocytes and their in vitro maturation. In contrast, the reduced melatonin level caused by siRNA to knockdown AANAT (siAANAT) is associated with the abnormal distribution of mitochondria, decreasing the ATP level of porcine oocytes and inhibiting their in vitro maturation. These abnormalities can be rescued by melatonin supplementation. In addition, we found that siAANAT switches the mitochondrial oxidative phosphorylation to glycolysis, a Warburg effect. This metabolic alteration can also be corrected by melatonin supplementation. All these activities of melatonin appear to be mediated by its membrane receptors since the non-selective melatonin receptor antagonist Luzindole can blunt the effects of melatonin. Taken together, the mitochondria of porcine oocytes can synthesize melatonin and improve the quality of oocyte maturation. These results provide an insight from a novel aspect to study oocyte maturation under in vitro conditions. [ABSTRACT FROM AUTHOR]

Currently, the integration of new energy sources into the power system poses a significant challenge to frequency stability. To address the issue of capacity sizing when utilizing storage battery systems to assist the power grid in frequency control, a capacity optimal allocation model is proposed for the primary frequency regulation of energy storage. Due to the requirement of a large number of actual parameters for the optimal allocation model, a simulation model of energy storage capacity is constructed based on the characteristics of primary frequency control to provide the necessary parameters. Subsequently, the primary frequency modulation output model of energy storage is established by considering the basic action output, the action in the frequency modulation dead zone, and a certain capacity margin. The antlion algorithm is employed to solve the capacity optimal allocation model. Finally, three groups of experiments are designed and compared to demonstrate the effectiveness of the proposed method in setting the capacity margin, which can increase profit to a certain extent. [ABSTRACT FROM AUTHOR]

Electricity price forecasting is a crucial aspect of spot trading in the electricity market and optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity price sequences often results in low accuracy in electricity price forecasting. To address this issue, this study proposes a quadratic hybrid decomposition method based on ensemble empirical modal decomposition (EEMD) and wavelet packet decomposition (WPD), along with a deep extreme learning machine (DELM) optimized by a THPO algorithm to enhance the accuracy of electricity price prediction. To overcome the problem of the optimization algorithm falling into local optima, an improved optimization algorithm strategy is proposed to enhance the optimization-seeking ability of HPO. The electricity price series is decomposed into a series of components using EEMD decomposition and WPD decomposition, and the DELM model optimized by the THPO algorithm is built for each component separately. The predicted values of all the series are then superimposed to obtain the final electricity price prediction. The proposed prediction model is evaluated using electricity price data from an Australian electricity market. The results demonstrate that the proposed improved algorithm strategy significantly improves the convergence performance of the algorithm, and the proposed prediction model effectively enhances the accuracy and stability of electricity price prediction, as compared to several other prediction models. [ABSTRACT FROM AUTHOR]

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In order to reduce the nitrogen oxides (NOx) emission of flue gas, a selective catalytic reduction (SCR) system must be installed. In general, the lag of the inlet NOx analyzer, the action of the NH3 injection valve and the feedforward signal are seriously delayed. Therefore, it is necessary to consider the measurement lag of inlet NOx on the NH3 injection flowrate control system. In this paper, the data-driven model of inlet NOx is proposed to improve control system, so as to avoid excessive or insufficient NH3 injection. First, the measurement lag time of inlet NOx is estimated by the blowback signal of a CEMS and the change process of the inlet O2 content. Then, an exponential model is used to predict the inlet NOx in advance, and recursive LSSVM is proposed to revise the output of the exponential model. Finally, the output of the final model is used as the feedforward signal for improved feedforward (IF) control. Based on IF control and PID control, the IF-PID control strategy for NH3 injection is proposed. The results show that the outlet NOx are close to the set value and meet the national environmental regulation. Furthermore, the average value of the NH3 injection flowrate remains unchanged. It shows that a better control effect and environmental sustainability are achieved without increasing the cost of NH3 injection. [ABSTRACT FROM AUTHOR]

In order to control NH3 injection for the selective catalytic reduction of nitrogen oxide (NOx) denitration (SCR de-NOx) process, a model that can accurately and quickly predict outlet NOx emissions is required. This paper presents a dynamic kernel partial least squares (KPLS) model incorporated with delay estimation and variable selection for outlet NOx emission and investigated control strategy for NH3 injection. First, k-nearest neighbor mutual information (KNN_MI) was used for delay estimation, and the effect of historical data lengths on KNN_MI was taken into account. Bidirectional search based on the change rate of KNN_MI (KNN_MI_CR) was used for variable selection. Delay–time difference update algorithm and feedback correction strategy were proposed. Second, the NH3 injection compensator (NIC) and the outlet NOx emission model constituted a correction controller. Then, its output and the output of the existing controller are added up to suitable NH3 injection. Finally, the KNN_MI_CR method was compared with different algorithms by benchmark dataset. The field data results showed that the KNN_MI_CR method could improve model accuracy for reconstructed samples. The final model can predict outlet NOx emissions in different operating states accurately. The control result not only meets the NOx emissions standard (50 mg/m3) but also keeps high de-NOx efficiency (80%). NH3 injection and NH3 escape are reduced by 11% and 39%. [ABSTRACT FROM AUTHOR]

The selective catalytic reduction (SCR) decomposition of nitrogen oxide (de-NO x ) process in coal-fired power plants not only displays nonlinearity, large inertia and time variation but also a lag in NO x analysis; hence, it is difficult to obtain an accurate model that can be used to control NH 3 injection during changes in the operating state. In this work, a novel dynamic inferential model with delay estimation was proposed for NO x emission prediction. First, k -nearest neighbour mutual information was used to estimate the time delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square was used to improve the prediction ability, and this was followed by verification using benchmark dataset experiments. Finally, the delay time difference method and feedback correction strategy were proposed to deal with the time variation of the SCR de-NO x process. Through the analysis of the experimental field data in the steady state, the variable state and the NO x analyser blowback process, the results proved that this dynamic model has high prediction accuracy during state changes and can realize advance prediction of the NO x emission., Competing Interests: There are no competing interests to declare., (© 2020 The Authors.)