Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability

Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models

Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models

Dynamic response and limit analysis of buried gas pipeline under ground consolidation load

Currently, the significant dynamic plastic deformation of a buried gas pipeline frequently occurs due to the ground construction process that acts as a direct threat to the operation security of a buried gas transmission system. In this study, the pipe-soil interaction structure under a dynamic consolidation load, such as high energy dynamic compaction load, was considered as a non-conservative system in the work. Two parts of structure dissipation energy were introduced into the Lagrange function, and the elastoplastic dynamic equations of a non-conservative system based on the Hamilton Variation Principle (HVP) and the finite element (FE) theory were established. Implicit solution schemes were proposed based on the dynamic equations, and a steel weight-soil-buried pipeline finite element model was developed by performing a dynamic analysis in the LS-DYNA software with an explicit format. Vivid impact responses of an underground pipeline associated with the buried depth, wall thickness, and tamping energy were simulated. The plastic failure criterion of high toughness pipeline steel indicates that treated pipeline buried depth, wall thickness, and tamping energy corresponded to the generalized loads, and limit state of a specific case. So, they were recognized via the relationship of generalized load in relation to the total strain of pipelines. This was performed by using tangent intersection criteria, two elastic slope criteria, and zero curvature criteria. Additionally, the von Mises yield stress criterion was also applied as a traditional approach. The study potentially offers significant references on the quantitative pre-evaluation of a buried gas pipeline that poses as a threat due to the occurrence of third-party damage such as extreme strong ground interference

Association between adipocytokines and diabetic retinopathy: a systematic review and meta-analysis

BackgroundDiabetic retinopathy (DR) is a common complication of diabetes. The adipocytokines are closely associated with the occurrence and development of diabetes and its related complications. Literature confirms that the level of adiponectin in patients with DR is significantly higher; however, the relationship between other adipocytokines (leptin, chemerin, apelin, and omentin-1) and DR remains unclear.AimThis study aimed to systematically evaluate the association between adipocytokines (leptin, chemerin, apelin, and omentin-1) and DR.MethodsThe PubMed, Web of Science, Embase, EBSCO and Willy databases were used to search for potential studies with keywords such as “diabetic retinopathy” or “DR” in combination with the terms “leptin,” “chemerin”, “apelin” or “omentin-1” in the search titles or abstracts. Standardized mean differences (SMD) with corresponding 95% confidence intervals (CIs) were determined as the results of the meta-analysis.ResultsAfter screening, 18 articles were included in the meta-analysis including 750 DR cases and 993 controls. Leptin and chemerin levels in patients with DR were significantly higher than those in the control group (SMD: 0.68, 95% CI [0.1, 1.26]; SMD: 0.79, 95% CI [0.35, 1.23]). The omentin-1 levels in patients with DR were significantly lower than those in the controls (SMD: –0.85, 95% CI [–1.08, –0.62]).ConclusionsTo the best of our knowledge, this is the first meta-analysis to evaluate the leptin, chemerin, apelin, and omentin-1 levels in patients with DR. Further high-quality studies are warranted to support the association between these adipocytokines and DR.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=443770, identifier CRD42023443770

Legume Lectin FRIL Preserves Neural Progenitor Cells in Suspension Culture In Vitro

In vitro maintenance of stem cells is crucial for many clinical applications. Stem cell preservation factor FRIL (Flt3 receptor-interacting lectin) is a plant lectin extracted from Dolichos Lablab and has been found preserve hematopoietic stem cells in vitro for a month in our previous studies. To investigate whether FRIL can preserve neural progenitor cells (NPCs), it was supplemented into serum-free suspension culture media. FRIL made NPC grow slowly, induced cell adhesion, and delayed neurospheres formation. However, FRIL did not initiate NPC differentiation according to immunofluorescence and semiquantitive RT-PCR results. In conclusion, FRIL could also preserve neural progenitor cells in vitro by inhibiting both cell proliferation and differentiation

The Proteasome Activators Blm10/PA200 Enhance the Proteasomal Degradation of N-Terminal Huntingtin.

The Blm10/PA200 family of proteasome activators modulates the peptidase activity of the core particle (20S CP). They participate in opening the 20S CP gate, thus facilitating the degradation of unstructured proteins such as tau and Dnm1 in a ubiquitin- and ATP-independent manner. Furthermore, PA200 also participates in the degradation of acetylated histones. In our study, we use a combination of yeast and human cell systems to investigate the role of Blm10/PA200 in the degradation of N-terminal Huntingtin fragments (N-Htt). We demonstrate that the human PA200 binds to N-Htt. The loss of Blm10 in yeast or PA200 in human cells results in increased mutant N-Htt aggregate formation and elevated cellular toxicity. Furthermore, Blm10 in vitro accelerates the proteasomal degradation of soluble N-Htt. Collectively, our data suggest N-Htt as a new substrate for Blm10/PA200-proteasomes and point to new approaches in Huntington\u27s disease (HD) research

Anti-Inflammatory Effect of Feiyangchangweiyan Capsule on Rat Pelvic Inflammatory Disease through JNK/NF- κ

Objectives. In this study, we aimed to illustrate the preventive effect and possible mechanisms of Feiyangchangweiyan capsule (FYCWYC) on rat pelvic inflammatory disease (PID) model. Methods. To construct the rat PID model, upper genital tract was infected by multipathogen, and then drugs were orally administered for 8 days. The histological examination, immunohistochemical analysis, and ELISA were carried out. Furthermore, Western blotting was used to analyze the expression of Akt, MAPKs, NF-κB p65, and IκB-α in uterus. Results. As the results showed, infiltrations of neutrophils and lymphocytes in uterus were significantly suppressed, and IL-1β, IL-6, CXCL-1, and TNF-α were also reduced in a dose-dependent manner. We also found that FYCWYC inhibited apoptosis induced by infection. Furthermore, FYCWYC could block the infection-induced nuclear translocation of NF-κB. We found that FYCWYC treatment only decreased the phosphorylation of JNK induced by infection and had no effects on Akt and P38. Additional, the effects of SP600125, an inhibitor of phospho-JNK, were similar to the results of FYCWYC. Conclusions. Taken together, our results demonstrated that FYCWYC had anti-inflammatory effect in pathogen-induced PID model, and the mechanism might be through inhibiting NF-κB nuclear translocation which is mediated by JNK

Proteasomes associated with the Blm10 activator protein antagonize mitochondrial fission through degradation of the fission protein Dnm1

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