Economic Evaluation of Post-Combustion CO2 Capture Integration Technology in Natural Gas Combined Cycle Power Plant

[Introduction] In recent years, natural gas power generation has played an important role in the construction of clean energy system of China. By the end of the "14th Five-year Plan" in 2025, China's gas power installed capacity is expected to hit 150 million kilowatts. Carbon capture，utilization and storage (CCUS) is one of the key paths for gas power to achieve the carbon peaking and carbon neutrality goals. [Method] To this end, an integrated plant combining 600 MW natural gas combined cycle (NGCC) and CO2 post-combustion capture (PCC) were set up as the simulation object. [Result] The simulation study shows that the design captures all CO2 flue gas with 90% efficiency, the CO2 compression and purification rate is 99.5%, the total output of gas power generation decreases by about 16.05%, the auxiliary power ratio increases by 5.55%, and the demand for circulating cooling water increases by about 50.52%. [Conclusion] The economic analysis shows that the static investment cost of the integrated plant is 54.28% higher than that of the single power plant, and the levelized cost of energy (LCOE) increases by 15.96%, which brings great difficulties to the deployment and development of carbon dioxide capture. However, the natural gas price is still the most important factor affecting the operating cost of the power plant

Assessing Reproducibility of Inherited Variants Detected With Short-Read Whole Genome Sequencing

Background: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. Results: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when \u3e 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×. Conclusions: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing

Background: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. Results: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30x. Conclusions: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.Peer reviewe

Financial Time Series Forecasting with the Deep Learning Ensemble Model

With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition of the importance of financial time series forecasting in financial market operation and management. In this paper, we propose a new financial time series forecasting model based on the deep learning ensemble model. The model is constructed by taking advantage of a convolutional neural network (CNN), long short-term memory (LSTM) network, and the autoregressive moving average (ARMA) model. The CNN-LSTM model is introduced to model the spatiotemporal data feature, while the ARMA model is used to model the autocorrelation data feature. These models are combined in the ensemble framework to model the mixture of linear and nonlinear data features in the financial time series. The empirical results using financial time series data show that the proposed deep learning ensemble-based financial time series forecasting model achieved superior performance in terms of forecasting accuracy and robustness compared with the benchmark individual models

A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction

Relational triple extraction, a fundamental procedure in natural language processing knowledge graph construction, assumes a crucial and irreplaceable role in the domain of academic research related to information extraction. In this paper, we propose a Double-Headed Entities and Relations Prediction (DERP) framework, which divides the entity recognition process into two stages: head entity recognition and tail entity recognition, using the obtained head and tail entities as inputs. By utilizing the corresponding relation and the corresponding entity, the DERP framework further incorporates a triple prediction module to improve the accuracy and completeness of the joint relation triple extraction. We conducted experiments on two English datasets, NYT and WebNLG, and two Chinese datasets, DuIE2.0 and CMeIE-V2, and compared the English dataset experimental results with those derived from ten baseline models. The experimental results demonstrate the effectiveness of our proposed DERP framework for triple extraction

Nomogram for predicting postoperative pulmonary complications in spinal tumor patients

Abstract Objectives Although several independent risk factors for postoperative pulmonary complications (PPCs) after spinal tumor surgery have been studied, a simple and valid predictive model for PPC occurrence after spinal tumor surgery has not been developed. Patients and methods We collected data from patients who underwent elective spine surgery for a spinal tumor between 2013 and 2020 at a tertiary hospital in China. Data on patient characteristics, comorbidities, preoperative examinations, intraoperative variables, and clinical outcomes were collected. We used univariable and multivariable logistic regression models to assess predictors of PPCs and developed and validated a nomogram for PPCs. We evaluated the performance of the nomogram using the area under the receiver operating characteristic curve (ROC), calibration curves, the Brier Score, and the Hosmer–Lemeshow (H–L) goodness-of-fit test. For clinical use, decision curve analysis (DCA) was conducted to identify the model’s performance as a tool for supporting decision-making. Results Among the participants, 61 (12.4%) individuals developed PPCs. Clinically significant variables associated with PPCs after spinal tumor surgery included BMI, tumor location, blood transfusion, and the amount of blood lost. The nomogram incorporating these factors showed a concordance index (C-index) of 0.755 (95% CI: 0.688–0.822). On internal validation, bootstrapping with 1000 resamples yielded a bias-corrected area under the receiver operating characteristic curve of 0.733, indicating the satisfactory performance of the nomogram in predicting PPCs. The calibration curve demonstrated accurate predictions of observed values. The decision curve analysis (DCA) indicated a positive net benefit for the nomogram across most predicted threshold probabilities. Conclusions We have developed a new nomogram for predicting PPCs in patients who undergo spinal tumor surgery

Table_1_The bibliometric and altmetric analysis of chronic traumatic encephalopathy research: how great is the impact?.DOCX

BackgroundThe study of chronic traumatic encephalopathy (CTE) has received great attention from academia and the general public. This study aims to analyze the research productivity on CTE and investigate the most discussed articles in academia and the general public by conducting bibliometric and altmetric analyses.MethodsData of articles were obtained from the Web of Science Core Databases and Altmetric Explore. VOSviewer and CiteSpace software were used to analyze and visualize the articles. The correlation between Altmetric attention scores (AAS) and citation counts were assessed by Spearman correlation coefficient.Results788 publications of CTE were eventually gathered and analyzed, and 100 articles with highest citation counts (Top-cited) and 100 articles with highest AASs (Top-AAS) were then identified. The keywords density map showed both the general public and the scientists were particularly interested in the risk factors and pathology of CTE, and scientists were interested in the causes and characteristics of neurodegenerative diseases while the public became increasingly concerned about the detection and prevention of CTE. By examining the shared characteristics of the 44 articles (High-High articles) that overlapped between Top-cited and Top-AAS articles, we identified certain traits that may potentially contribute to their high citation rates and high AASs. Besides, significant positive correlations with varied strength between AAS and citation were observed in the 788 articles, Top-cited, Top-AAS and High-High datasets.ConclusionThis study is the first to link bibliometric and altmetric analyses for CTE publications, which may provide deeper understanding of the attention of the scientists and the general public pay to the study of CTE, and offer some guidance and inspiration for future CTE in the selection of research topics and directions.</p

Anodic Cross-Coupling of Biomass Platform Chemicals to Sustainable Biojet Fuel Precursors

Electrocatalytic conversion of biomass platform chemicals to jet fuel precursors is a promising approach to alleviate the energy crisis caused by the excessive exploitation and consumption of non-renewable fossil fuels. However, an aqueous electrolyte has been rarely studied. In this study, we demonstrate an anodic electrocatalysis route for producing jet fuel precursors from biomass platform chemicals on Ni-based electrocatalysts in an aqueous electrolyte at room temperature and atmosphere pressure. The desired product exhibited high selectivity for the jet fuel precursor (95.4%) and an excellent coulombic efficiency of 210%. A series of in situ characterizations demonstrated that Ni2+ species were the active sites for the coupling process. In addition, the coupling reaction could be achieved by generating radical cations and inhibiting the side reaction. First, the electrochemical process could activate the furfural (FF) molecule and generate radical cations, resulting in an average of 2.0 times chain propagation. The levulinic acid (LA) molecules played a vital role in the coupling reaction. The adsorption strength of LA on Ni3N was higher than that of FF, which could inhibit the side reaction (the oxidation of FF) and achieve high selectivity. Meanwhile, the LA molecules were adsorbed on the Ni3N surface and then disrupted the formation of Ni3+ species, thus favoring the coupling reaction. This work demonstrates an efficient route to produce jet fuel precursors directly from biomass platform chemicals and provides a comprehensive understanding of the anodic coupling process