This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even br...This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.展开更多
The disintegration of networks is a widely researched topic with significant applications in fields such as counterterrorism and infectious disease control. While the traditional approaches for achieving network disin...The disintegration of networks is a widely researched topic with significant applications in fields such as counterterrorism and infectious disease control. While the traditional approaches for achieving network disintegration involve identifying critical sets of nodes or edges, limited research has been carried out on edge-based disintegration strategies. We propose a novel algorithm, i.e., a rank aggregation elite enumeration algorithm based on edge-coupled networks(RAEEC),which aims to implement tiling for edge-coupled networks by finding important sets of edges in the network while balancing effectiveness and efficiency. Our algorithm is based on a two-layer edge-coupled network model with one-to-one links, and utilizes three advanced edge importance metrics to rank the edges separately. A comprehensive ranking of edges is obtained using a rank aggregation approach proposed in this study. The top few edges from the ranking set obtained by RAEEC are then used to generate an enumeration set, which is continuously iteratively updated to identify the set of elite attack edges.We conduct extensive experiments on synthetic networks to evaluate the performance of our proposed method, and the results indicate that RAEEC achieves a satisfactory balance between efficiency and effectiveness. Our approach represents a significant contribution to the field of network disintegration, particularly for edge-based strategies.展开更多
<strong>Objective:</strong><span style="font-family:""><span style="font-family:Verdana;"> This study aimed to identify hub genes that are associated with hepatocellula...<strong>Objective:</strong><span style="font-family:""><span style="font-family:Verdana;"> This study aimed to identify hub genes that are associated with hepatocellular carcinoma (HCC) prognosis by bioinformatics analysis. </span><b><span style="font-family:Verdana;">Methods:</span></b><span style="font-family:Verdana;"> Data were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) liver HCC datasets. </span><a name="_Hlk11768117"></a><span style="font-family:Verdana;">The robust rank ag</span><span style="font-family:Verdana;">gregation algorithm was used in integrating the data on differentially ex</span><span style="font-family:Verdana;">pressed genes (DEGs). Online databases DAVID 6.8 and REACTOME were used for </span><span style="font-family:Verdana;">gene ontology and pathway enrichment analysis. R software version 3.5.1, </span><span style="font-family:Verdana;">Cytoscape, and Kaplan-Meier plotter were used to identify hub genes. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> Six GEO datasets and the TCGA liver HCC dataset were included in this analysis. A total of 151 upregulated and 245 downregulated DEGs were iden</span><span style="font-family:Verdana;">tified. The upregulated DEGs most significantly enriched in the functional</span><span style="font-family:Verdana;"> categories of cell division, chromosomes, centromeric regions, and </span><span style="font-family:Verdana;">protein binding, whereas the downregulated DEGs most significantly</span><span style="font-family:Verdana;"> enriched in the </span><a name="_Hlk11059934"></a><span style="font-family:Verdana;">epoxygenase P450 pathway, extracellular region, and heme binding, with respect to biological process, cellular component, and molecular function analysis, respectively. Upregulated DEGS most significantly enriched the cell cycle pathway, whereas downregulated DEGs most significantly enriched </span><span style="font-family:Verdana;">the metabolism pathway. Finally, 88 upregulated and 40 downregulated genes were </span><span><span style="font-family:Verdana;">identified as hub genes. The top 10 upregulated hub DEGs were </span><i><span style="font-family:Verdana;">CDK</span></i><span style="font-family:Verdana;">1,</span></span><i><span style="font-family:Verdana;"> CCNB</span></i><span><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> CCNB</span></i><span style="font-family:Verdana;">2,</span><i><span style="font-family:Verdana;"> CDC</span></i><span style="font-family:Verdana;">20,</span><i><span style="font-family:Verdana;"> CCNA</span></i><span style="font-family:Verdana;">2,</span><i><span style="font-family:Verdana;"> AURKA</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> MAD</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">L</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> TOP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> BUB</span></i><span style="font-family:Verdana;">1</span><i><span style="font-family:Verdana;">B </span></i><span style="font-family:Verdana;">and</span></span><i> <span style="font-family:Verdana;">BUB</span></i><span><span style="font-family:Verdana;">1. The top 10 downregulated hub DEGs were </span><i><span style="font-family:Verdana;">ESR</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> IGF</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> FTCD</span></i><span style="font-family:Verdana;">,</span></span><i><span style="font-family:Verdana;"> CYP</span></i><span style="font-family:Verdana;">3</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">4,</span><i><span style="font-family:Verdana;"> SPP</span></i><span style="font-family:Verdana;">2,</span><i> <span style="font-family:Verdana;">C</span></i><span><span style="font-family:Verdana;">8</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> CYP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">E</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> TAT</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> F</span></i><span style="font-family:Verdana;">9 and </span><i><span style="font-family:Verdana;">CYP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">C</span></i><span style="font-family:Verdana;">9. </span><b><span style="font-family:Verdana;">Conclusions:</span></b><span style="font-family:Verdana;"> This study identified</span></span><span style="font-family:Verdana;"> several upregulated and downregulated hub genes that are associated with the prognosis of HCC patients. Verification of these results using </span><i><span style="font-family:Verdana;">in vitro</span></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">in vivo</span></i><span style="font-family:Verdana;"> studies is warranted.</span></span>展开更多
基金supported by the National Natural Science Foundation of China(Grant No.72401207 and 42101300)Beijing Municipal Education Commission,China(Grant No.SM202110038001).
文摘This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.
基金supported by the National Natural Science Foundation of China (Grant Nos. 61877046, 12271419, and 62106186)the Natural Science Basic Research Program of Shaanxi (Program No. 2022JQ-620)the Fundamental Research Funds for the Central Universities (Grant Nos. XJS220709, JB210701, and QTZX23002)。
文摘The disintegration of networks is a widely researched topic with significant applications in fields such as counterterrorism and infectious disease control. While the traditional approaches for achieving network disintegration involve identifying critical sets of nodes or edges, limited research has been carried out on edge-based disintegration strategies. We propose a novel algorithm, i.e., a rank aggregation elite enumeration algorithm based on edge-coupled networks(RAEEC),which aims to implement tiling for edge-coupled networks by finding important sets of edges in the network while balancing effectiveness and efficiency. Our algorithm is based on a two-layer edge-coupled network model with one-to-one links, and utilizes three advanced edge importance metrics to rank the edges separately. A comprehensive ranking of edges is obtained using a rank aggregation approach proposed in this study. The top few edges from the ranking set obtained by RAEEC are then used to generate an enumeration set, which is continuously iteratively updated to identify the set of elite attack edges.We conduct extensive experiments on synthetic networks to evaluate the performance of our proposed method, and the results indicate that RAEEC achieves a satisfactory balance between efficiency and effectiveness. Our approach represents a significant contribution to the field of network disintegration, particularly for edge-based strategies.
文摘<strong>Objective:</strong><span style="font-family:""><span style="font-family:Verdana;"> This study aimed to identify hub genes that are associated with hepatocellular carcinoma (HCC) prognosis by bioinformatics analysis. </span><b><span style="font-family:Verdana;">Methods:</span></b><span style="font-family:Verdana;"> Data were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) liver HCC datasets. </span><a name="_Hlk11768117"></a><span style="font-family:Verdana;">The robust rank ag</span><span style="font-family:Verdana;">gregation algorithm was used in integrating the data on differentially ex</span><span style="font-family:Verdana;">pressed genes (DEGs). Online databases DAVID 6.8 and REACTOME were used for </span><span style="font-family:Verdana;">gene ontology and pathway enrichment analysis. R software version 3.5.1, </span><span style="font-family:Verdana;">Cytoscape, and Kaplan-Meier plotter were used to identify hub genes. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> Six GEO datasets and the TCGA liver HCC dataset were included in this analysis. A total of 151 upregulated and 245 downregulated DEGs were iden</span><span style="font-family:Verdana;">tified. The upregulated DEGs most significantly enriched in the functional</span><span style="font-family:Verdana;"> categories of cell division, chromosomes, centromeric regions, and </span><span style="font-family:Verdana;">protein binding, whereas the downregulated DEGs most significantly</span><span style="font-family:Verdana;"> enriched in the </span><a name="_Hlk11059934"></a><span style="font-family:Verdana;">epoxygenase P450 pathway, extracellular region, and heme binding, with respect to biological process, cellular component, and molecular function analysis, respectively. Upregulated DEGS most significantly enriched the cell cycle pathway, whereas downregulated DEGs most significantly enriched </span><span style="font-family:Verdana;">the metabolism pathway. Finally, 88 upregulated and 40 downregulated genes were </span><span><span style="font-family:Verdana;">identified as hub genes. The top 10 upregulated hub DEGs were </span><i><span style="font-family:Verdana;">CDK</span></i><span style="font-family:Verdana;">1,</span></span><i><span style="font-family:Verdana;"> CCNB</span></i><span><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> CCNB</span></i><span style="font-family:Verdana;">2,</span><i><span style="font-family:Verdana;"> CDC</span></i><span style="font-family:Verdana;">20,</span><i><span style="font-family:Verdana;"> CCNA</span></i><span style="font-family:Verdana;">2,</span><i><span style="font-family:Verdana;"> AURKA</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> MAD</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">L</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> TOP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> BUB</span></i><span style="font-family:Verdana;">1</span><i><span style="font-family:Verdana;">B </span></i><span style="font-family:Verdana;">and</span></span><i> <span style="font-family:Verdana;">BUB</span></i><span><span style="font-family:Verdana;">1. The top 10 downregulated hub DEGs were </span><i><span style="font-family:Verdana;">ESR</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> IGF</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> FTCD</span></i><span style="font-family:Verdana;">,</span></span><i><span style="font-family:Verdana;"> CYP</span></i><span style="font-family:Verdana;">3</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">4,</span><i><span style="font-family:Verdana;"> SPP</span></i><span style="font-family:Verdana;">2,</span><i> <span style="font-family:Verdana;">C</span></i><span><span style="font-family:Verdana;">8</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> CYP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">E</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> TAT</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> F</span></i><span style="font-family:Verdana;">9 and </span><i><span style="font-family:Verdana;">CYP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">C</span></i><span style="font-family:Verdana;">9. </span><b><span style="font-family:Verdana;">Conclusions:</span></b><span style="font-family:Verdana;"> This study identified</span></span><span style="font-family:Verdana;"> several upregulated and downregulated hub genes that are associated with the prognosis of HCC patients. Verification of these results using </span><i><span style="font-family:Verdana;">in vitro</span></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">in vivo</span></i><span style="font-family:Verdana;"> studies is warranted.</span></span>
