Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model

Short-term electric load is significantly affected by weather, especially the temperature effects in summer. External factors can result in mutation structures in load data. Under the influence of the external temperature factors, city electric load cannot be easily forecasted as usual. This research analyzes the relationship between electricity load and daily temperature in city. An improved ARIMAX model is proposed in this paper to deal with the mutation data structures. It is found that information amount of the improved ARIMAX model is smaller than that of the classic method and its relative error is less than AR, ARMA and SigmoidFunction ANN models. The forecasting results are more accurately fitted. This improved model is highly valuable when dealing with mutation data structure in the field of load forecasting. And it is also an effective technique in forecasting electric load with temperature effects

Identification of DHX36 as a tumour suppressor through modulating the activities of the stress-associated proteins and cyclin-dependent kinases in breast cancer

The nucleic acid guanine-quadruplex structures (G4s) are involved in many aspects of cancer progression. The DEAH-box polypeptide 36 (DHX36) has been identified as a dominant nucleic acid helicase which targets and disrupts DNA and RNA G4s in an ATP-dependent manner. However, the actual role of DHX36 in breast cancer remains unknown. In this study, we observed that the gene expression of DHX36 was positively associated with patient survival in breast cancer. The abundance of DHX36 is also linked with pathologic conditions and the stage of breast cancer. By using the xenograft mouse model, we demonstrated that the stable knockdown of DHX36 via lentivirus in breast cancer cells significantly promoted tumour growth. We also found that, after the DHX36 knockdown (KD), the invasion of triple-negative breast cancer cells was enhanced. In addition, we found a significant increase in the number of cells in the S-phase and a reduction of apoptosis with the response to cisplatin. DHX36 KD also desensitized the cytotoxic cellular response to paclitaxel and cisplatin. Transcriptomic profiling analysis by RNA sequencing indicated that DHX36 altered gene expression profile through the upstream activation of TNF, IFNγ, NFκb and TGFβ1. High throughput signalling analysis showed that one cluster of stress-associated kinase proteins including p53, ROCK1 and JNK were suppressed, while the mitotic checkpoint protein-serine kinases CDK1 and CDK2 were activated, as a consequence of the DHX36 knockdown. Our study reveals that DHX36 functions as a tumour suppressor and may be considered as a potential therapeutic target in breast cancer

Identification of DHX36 as a tumour suppressor through modulating the activities of the stress-associated proteins and cyclin-dependent kinases in breast cancer

The nucleic acid guanine-quadruplex structures (G4s) are involved in many aspects of cancer progression. The DEAH-box polypeptide 36 (DHX36) has been identified as a dominant nucleic acid helicase which targets and disrupts DNA and RNA G4s in an ATP-dependent manner. However, the actual role of DHX36 in breast cancer remains unknown. In this study, we observed that the gene expression of DHX36 was positively associated with patient survival in breast cancer. The abundance of DHX36 is also linked with pathologic conditions and the stage of breast cancer. By using the xenograft mouse model, we demonstrated that the stable knockdown of DHX36 via lentivirus in breast cancer cells significantly promoted tumour growth. We also found that, after the DHX36 knockdown (KD), the invasion of triple-negative breast cancer cells was enhanced. In addition, we found a significant increase in the number of cells in the S-phase and a reduction of apoptosis with the response to cisplatin. DHX36 KD also desensitized the cytotoxic cellular response to paclitaxel and cisplatin. Transcriptomic profiling analysis by RNA sequencing indicated that DHX36 altered gene expression profile through the upstream activation of TNF, IFNγ, NFκb and TGFβ1. High throughput signalling analysis showed that one cluster of stress-associated kinase proteins including p53, ROCK1 and JNK were suppressed, while the mitotic checkpoint protein-serine kinases CDK1 and CDK2 were activated, as a consequence of the DHX36 knockdown. Our study reveals that DHX36 functions as a tumour suppressor and may be considered as a potential therapeutic target in breast cancer

Inhibition of Protein Phosphatase 2A Sensitizes Mucoepidermoid Carcinoma to Chemotherapy via the PI3K-AKT Pathway in Response to Insulin Stimulus

Background/Aims: Protein phosphatase 2A (PP2A) is a ubiquitous serine/threonine phosphatase that mediates cell cycle regulation and metabolism. Mounting evidence has indicated that PP2A inhibition exhibits considerable anticancer potency in multiple types of human cancers. However, the efficacy of PP2A inhibition remains unexplored in mucoepidermoid carcinoma (MEC), especially in locally advanced and metastatic cases with limited systemic treatment. In this study, we demonstrated the therapeutic potency of LB100 in mucoepidermoid carcinoma. Methods: In this study, the expression of PP2A was evaluated using immunohistochemical (IHC) staining. The effects associated with LB100 alone and in combination with cisplatin for the treatment of mucoepidermoid carcinoma were investigated both in vitro, regarding metabolism, proliferation, and migration, and in vivo in a mucoepidermoid carcinoma xenograft model. In addition, with LB100 treatment and in response to an insulin stimulus, the expression levels and phosphorylation levels of targets in the PI3K-AKT pathway were determined using western blot analysis and immunoblotting. Results: The expression of protein phosphatase 2A was significantly upregulated in the clinical specimens of high-grade MECs compared with those of low-/medium-grade MECs and normal controls. In this article, we report that a small molecule PP2A inhibitor, LB100, decreased cellular viability and glycolytic activity and induced G2/M cell cycle arrest. Importantly, LB100 enhanced the efficacy of cisplatin in mucoepidermoid carcinoma cells both in vitro and in vivo. PP2A inhibition by LB100 increased the phosphorylation of insulin receptor substrate 1(IRS-1) on serine residues, downregulated the expression of phosphatidylinositol 3-kinase (PI3K) p110 alpha subunit and dephosphorylated AKT at Ser473 and Thr308 in mucoepidermoid carcinoma cells in response to insulin stimulus. Conclusion: These results highlight the translational potential of PP2A inhibition to synergize with cisplatin in mucoepidermoid carcinoma treatment

Decomposition and Forecasting of CO2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model

China faces significant challenges related to global warming caused by CO2 emissions, and the power industry is a large CO2 emitter. The decomposition and accurate forecasting of CO2 emissions in China’s power sector are thus crucial for low-carbon outcomes. This paper selects seven socio-economic and technological drivers related to the power sector, and decomposes CO2 emissions based on two models: the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model and the partial least square (PLS) model. Distinguished from previous research, our study first compares the effects of eliminating the multicollinearity of the PLS model with stepwise regression and ridge regression, finding that PLS is superior. Further, the decomposition results show the factors’ absolute elasticity coefficients are population (2.58) > line loss rate (1.112) > GDP per capita (0.669) > generation structure (0.522) > the urbanization level (0.512) > electricity intensity (0.310) > industrial structure (0.060). Meanwhile, a novel hybrid PLS-Grey-Markov model is proposed, and is verified to have better precision for the CO2 emissions of the power sector compared to the selected models, such as ridge regression-Grey-Markov, PLS-Grey-Markov, PLS-Grey and PLS-BP (Back propagation neutral network model). The forecast results suggest that CO2 emissions of the power sector will increase to 5102.9 Mt by 2025. Consequently, policy recommendations are proposed to achieve low-carbon development in aspects of population, technology, and economy

Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model

Short-term electric load is significantly affected by weather, especially the temperature effects in summer. External factors can result in mutation structures in load data. Under the influence of the external temperature factors, city electric load cannot be easily forecasted as usual. This research analyzes the relationship between electricity load and daily temperature in city. An improved ARIMAX model is proposed in this paper to deal with the mutation data structures. It is found that information amount of the improved ARIMAX model is smaller than that of the classic method and its relative error is less than AR, ARMA and Sigmoid-Function ANN models. The forecasting results are more accurately fitted. This improved model is highly valuable when dealing with mutation data structure in the field of load forecasting. And it is also an effective technique in forecasting electric load with temperature effects

Decomposition and Forecasting of CO₂ Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model

China faces significant challenges related to global warming caused by CO2 emissions, and the power industry is a large CO2 emitter. The decomposition and accurate forecasting of CO2 emissions in China’s power sector are thus crucial for low-carbon outcomes. This paper selects seven socio-economic and technological drivers related to the power sector, and decomposes CO2 emissions based on two models: the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model and the partial least square (PLS) model. Distinguished from previous research, our study first compares the effects of eliminating the multicollinearity of the PLS model with stepwise regression and ridge regression, finding that PLS is superior. Further, the decomposition results show the factors’ absolute elasticity coefficients are population (2.58) > line loss rate (1.112) > GDP per capita (0.669) > generation structure (0.522) > the urbanization level (0.512) > electricity intensity (0.310) > industrial structure (0.060). Meanwhile, a novel hybrid PLS-Grey-Markov model is proposed, and is verified to have better precision for the CO2 emissions of the power sector compared to the selected models, such as ridge regression-Grey-Markov, PLS-Grey-Markov, PLS-Grey and PLS-BP (Back propagation neutral network model). The forecast results suggest that CO2 emissions of the power sector will increase to 5102.9 Mt by 2025. Consequently, policy recommendations are proposed to achieve low-carbon development in aspects of population, technology, and economy

Co-Evolution Analysis on Coal-Power Industries Cluster Ecosystem Based on the Lotka-Volterra Model: A Case Study of China

This contradiction caused by differences in coal-electricity industry market forces is “market for coal, plans for electricity". The traditional coal and power enterprises cause serious pollution problems and ecological problems in the production process, restricting the sustainable socio-economic development. The coal-electricity industry cluster ecosystem forms may effectively mediate organizations conflict existing in the development of industrial clusters, improving resource utilization, reducing energy consumption, to achieve harmony with the natural environment. This paper combines with the theory of industrial ecology, with the Lotka-Volterra model, to explore co-evolution mechanism of coal–electricity industry cluster at the micro, meso and macro levels. In the end, this paper adapted the "Henon mapping" method to describe the chaos phenomenon existed in the development. This paper put forward specific development model for industrial clusters in lower, steady and advanced stages. The co-evolution coal-electricity industry cluster ecosystem is a good solution to numerous conflicts

An Investment Feasibility Analysis of CCS Retrofit Based on a Two-Stage Compound Real Options Model

Carbon capture and storage (CCS) technology is an attractive technique to help power enterprises with carbon emission reduction. In this paper, a two-stage CCS retrofit investment in an existing coal-fired power plant in China including the first stage (demonstration project) and second stage (commercial operation) is taken as a case to decide when and whether to invest. Distinguished from previous models, a binomial lattice compound real options model including the options to defer and expand is established. Further, the accounting approaches to certified emission reductions (CERs) based on the thermodynamics principle are first proposed concerning this model. We find the total invest value under compound options model is less than zero, although greater than that by NPV method. The results indicate carbon prices and subsidy policy, respectively, play a dominating role in initiating the CCS investment at the first and second stage. The growth in government subsidy at the first stage has obviously greater effects on decreasing critical carbon trading prices. Besides, the minimum critical carbon price is 87.09 RMB/ton with full subsidy, greater than the current price (56 RMB/ton). This also illustrates it is not the optimal occasion to invest in a CCS retrofit project for power enterprises