Experimental study on the methane explosion suppression by ultra-fine water mist containing bacteria under degradation for five times.

In a semi-closed visualization pipeline, this experiment studied the inhibitory effect of ultra-fine pure water mist, ultra-fine water mist containing inorganic salt and ultra-fine water mist containing bacteria-inorganic salt on 9.8% methane explosion under five different quality of spray volume. Combined with the methane explosion suppression experiment, the ability of methane-oxidizing bacteria to degrade 9.8% of methane was studied in a simulated pipeline. Experiments showed that the addition of inorganic salt and the degradation of methane-oxidizing bacteria could improve the suppression explosion effect of ultra-fine water mist, and the suppression explosion effect was related to the volume of water mist. Under the same ultra-fine water mist condition, with the increase of the volume of water mist, the explosion suppression effect was improved. Compared with pure methane, pure water ultra-fine water mist, and inorganic salt ultra-fine water mist, the maximum explosion overpressure and flame propagation speed under the condition of bacteria-inorganic salt ultra-fine water mist were significantly reduced. Compared with the explosion of pure methane, due to the degradation of methane by methane-oxidizing bacteria, when the degradation time was 10 h, and the volume of ultra-fine water mist containing bacteria-inorganic salt was 12.5 mL, the maximum explosion overpressure dropped significantly from 0.663 to 0.343 MPa, a decrease of 48.27%. The appearance time of the maximum explosion overpressure was delayed from 208.8 to 222.6 ms. The peak flame velocity was 4 m s-1, which was 83.3% lower than that of 9.8% pure methane explosion. This study will contribute to the development of efficient ultrafine water mist synergistic inhibitors for the prevention of methane explosion disasters.

Safety risk assessment of sustainable construction based on projection pursuit model optimized by multi-intelligent algorithm: a case study of new chemical projects.

With the rapid development of urban and social economies, the safety accidents in the construction process of the new chemical plant have caused huge losses to the city. The purpose of this study is to evaluate the risks in the construction process of chemical projects and propose preventive measures. A novel risk assessment model based on multi-intelligence algorithm optimization projection pursuit was developed to assess the construction safety risk and determine the risk level. In this model, the best-worst method and the entropy weight method were used as subjective and objective evaluation methods, respectively. The theory based on the idea of the distance function was applied to the model to calculate the combined weight value. The results showed that the three evaluation objects with the highest risk value were the air compression station plant, regional control room, and hazardous and solid waste temporary repository. The risk values of these three buildings were 2.2557, 2.2160, and 2.1654, respectively, and the corresponding risk level was high. On-site safety managers should take immediate measures in these high-risk buildings to reduce the possibility of accidents. This study is a new attempt to consider the construction safety risk of the new chemical project.

Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma.

BACKGROUND: There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma (PHC). Previous reports have shown that over 10% of patients with PHC experience postoperative pulmonary infections. Thus, it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC. AIM: To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management. METHODS: We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery. Radiomics data were selected for statistical analysis, and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables. We then developed a pulmonary infection prediction model using three different models: An artificial neural network model; a random forest model; and a generalized linear regression model. Finally, we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses. RESULTS: Among the 505 patients, 86 developed a postoperative pulmonary infection, resulting in an incidence rate of 17.03%. Based on the gray-level co-occurrence matrix, we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models. Among these, energy, contrast, the sum of squares (SOS), the inverse difference (IND), mean sum (MES), sum variance (SUV), sum entropy (SUE), and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models. The random forest model algorithm, in combination with IND, SOS, MES, SUE, SUV, and entropy, demonstrated the highest prediction efficiency in both the training and internal verification sets, with areas under the curve of 0.823 and 0.801 and a 95% confidence interval of 0.766-0.880 and 0.744-0.858, respectively. The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95% confidence intervals of 0.677-0.791 and 0.766-0.864, respectively. CONCLUSION: Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND, SOS, MES, SUE, SUV, energy, and entropy. The prediction model in this study based on diffusion-weighted images, especially the random forest model algorithm, can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.

Micromagnetic and Robust Evaluation of Surface Hardness in Cr12MoV Steel Considering Repeatability of the Instrument.

The combination of multifunctional micromagnetic testing and neural network-based prediction models is a promising way of nondestructive and quantitative measurement of steel surface hardness. Current studies mainly focused on improving the prediction accuracy of intelligent models, but the unavoidable and random uncertainties related to instruments were seldom explored. The robustness of the prediction model considering the repeatability of instruments was seldom discussed. In this work, a self-developed multifunctional micromagnetic instrument was employed to perform the repeatability test with Cr12MoV steel. The repeatability of the instrument in measuring multiple magnetic features under both static and dynamic conditions was evaluated. The magnetic features for establishing the prediction model were selected based on the consideration of both the repeatability of the instrument and the ability of magnetic features in surface hardness evaluation. To improve the robustness of the model in surface hardness prediction, a modelling strategy considering the repeatability of the instrument was proposed. Through removing partial magnetic features with higher mean impact values from input nodes, robust evaluation of surface hardness in Cr12MoV steel was realized with the multifunctional micromagnetic instrument.

Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy.

BACKGROUND: Postoperative pancreatic fistula (PF) is a serious life-threatening complication after pancreaticoduodenectomy (PD). Our research aimed to develop a machine learning (ML)-aided model for PF risk stratification. AIM: To develop an ML-aided model for PF risk stratification. METHODS: We retrospectively collected 618 patients who underwent PD from two tertiary medical centers between January 2012 and August 2021. We used an ML algorithm to build predictive models, and subject prediction index, that is, decision curve analysis, area under operating characteristic curve (AUC) and clinical impact curve to assess the predictive efficiency of each model. RESULTS: A total of 29 variables were used to build the ML predictive model. Among them, the best predictive model was random forest classifier (RFC), the AUC was [0.897, 95% confidence interval (CI): 0.370-1.424], while the AUC of the artificial neural network, eXtreme gradient boosting, support vector machine, and decision tree were between 0.726 (95%CI: 0.191-1.261) and 0.882 (95%CI: 0.321-1.443). CONCLUSION: Fluctuating serological inflammatory markers and prognostic nutritional index can be used to predict postoperative PF.

Thermal hazard evaluation on spontaneous combustion characteristics of nitrocellulose solution under different atmospheric conditions.

Nitrocellulose (NC) is widely used in both military and civilian fields. Because of its high chemical sensitivity and low decomposition temperature, NC is prone to spontaneous combustion. Due to the dangerous properties of NC, it is often dissolved in other organic solvents, then stored and transported in the form of a solution. Therefore, this paper took NC solutions (NC-S) with different concentrations as research objects. Under different atmospheric conditions, a series of thermal analysis experiments and different reaction kinetic methods investigated the influence of solution concentration and oxygen concentration on NC-S's thermal stability. The variation rules of NC-S's thermodynamic parameters with solution and oxygen concentrations were explored. On this basis, the spontaneous combustion characteristics of NC-S under actual industrial conditions were summarized to put forward the theoretical guidance for the spontaneous combustion treatment together with the safety in production, transportation, and storage.

Physical model experiment on the influence of water depth on the underwater pipeline surface impacted by landslide surge.

A physical model experiment of flume block landslide was used to study the influence of landslide surge impact on underwater pipeline surface under different water depths. The influence of surge impact pressure on pipelines with different water depths and the impact pressure of surge at different angles of underwater pipelines wall were analyzed. And the relationship between the maximum impact pressure of underwater pipelines and the depth of water was obtained. The results indicated that with the decrease of the water depths, the maximum impact pressure at the wall of the underwater pipeline increases approximately linearly, and the slider is easier to form higher first wave height. The maximum impact pressure of the upper surface of the pipeline wall is greater than that of the lower surface of the pipeline wall under the same working conditions. It is also found that the smaller the depth of water, the larger the maximum pressure and average pressure at the measuring point would be and the greater the pressure fluctuation becomes when slider volume and landslide water inlet angle and speed remain the same.

Thermal Effect and Mechanism Analysis of Flame-Retardant Modified Polymer Electrolyte for Lithium-Ion Battery.

In recent years, the prosperous electric vehicle industry has contributed to the rapid development of lithium-ion batteries. However, the increase in the energy density of lithium-ion batteries has also created more pressing safety concerns. The emergence of a new flame-retardant material with the additive ethoxy (pentafluoro) cyclotriphosphazene can ameliorate the performance of lithium-ion batteries while ensuring their safety. The present study proposes a new polymer composite flame-retardant electrolyte and adopts differential scanning calorimetry (DSC) and accelerating rate calorimetry to investigate its thermal effect. The study found that the heating rate is positively correlated with the onset temperature, peak temperature, and endset temperature of the endothermic peak. The flame-retardant modified polymer electrolyte for new lithium-ion batteries has better thermal stability than traditional lithium-ion battery electrolytes. Three non-isothermal methods (Kissinger; Kissinger-Akahira-Sunose; and Flynn-Wall-Ozawa) were also used to calculate the kinetic parameters based on the DSC experimental data. The apparent activation energy results of the three non-isothermal methods were averaged as 54.16 kJ/mol. The research results can provide valuable references for the selection and preparation of flame-retardant additives in lithium-ion batteries.

Decreased NSG3 enhances PD-L1 expression by Erk1/2 pathway to promote pancreatic cancer progress.

Inhibiting the functioning of PD-1/PD-L1 to activate human immune system and improve the prognosis of pancreatic cancer (PC) would provide a significant boost to handling the disease. One research found the expression level of NSG3 was reduced in pediatric pilocytic astrocytoma, so is PC and we found NSG3 could regulate the expression of PD-L1. So NSG3 could become a new target for enhancing the immune response to PC. The GEPIA website was employed to analyze the prognoses in PC patients with different NSG3 levels. Immunohistochemistry (IHC) analysis was applied to detect different levels of NSG3 in para-PC and PC tissues. Cell biological function tests (in vitro) were performed and a subcutaneous nude mice tumor model (in vivo) was established to verify the effect of NSG3 on PC. Immunoblotting and RT-qPCR were utilized to demonstrate the inhibiting effect of NSG3 on PD-L1 through regulating Erk1/2 phosphorylation. A subcutaneous C57BL/6 tumor mice model was established to assess the possibility of a synergistic effect of NSG3 expression and the use of an anti-PD-L1 antibody on PC. PC tissues had decreased NSG3 expression levels, which led to poor prognosis. Overexpressing NSG3 suppressed proliferation, invasion and migration capacities of PC cells. On the contrary, knocking-down NSG3 prompted PC malignancy whether in vivo or in vitro. Importantly, NSG3 prevented Erk1/2 phosphorylation to inhibit PD-L1 expression. Additionally, NSG3 and an immune checkpoint inhibitor anti-PD-1 antibody acted synergistically, which enhanced the efficacy of the inhibitor. NSG3 inhibited PD-L1 expression by suppressing Erk1/2 phosphorylation to improve the immune response to PC. NSG3 is, therefore, a potential new diagnostic and prognostic marker, particularly useful in immune checkpoint blockade therapy.

Thermal Stability Analysis of Lithium-Ion Battery Electrolytes Based on Lithium Bis(trifluoromethanesulfonyl)imide-Lithium Difluoro(oxalato)Borate Dual-Salt.

Lithium-ion batteries with conventional LiPF6 carbonate electrolytes are prone to failure at high temperature. In this work, the thermal stability of a dual-salt electrolyte of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and lithium difluoro(oxalato)borate (LiODFB) in carbonate solvents was analyzed by accelerated rate calorimetry (ARC) and differential scanning calorimetry (DSC). LiTFSI-LiODFB dual-salt carbonate electrolyte decomposed when the temperature exceeded 138.5 °C in the DSC test and decomposed at 271.0 °C in the ARC test. The former is the onset decomposition temperature of the solvents in the electrolyte, and the latter is the LiTFSI-LiODFB dual salts. Flynn-Wall-Ozawa, Starink, and autocatalytic models were applied to determine pyrolysis kinetic parameters. The average apparent activation energy of the dual-salt electrolyte was 53.25 kJ/mol. According to the various model fitting, the thermal decomposition process of the dual-salt electrolyte followed the autocatalytic model. The results showed that the LiTFSI-LiODFB dual-salt electrolyte is significantly better than the LiPF6 electrolyte in terms of thermal stability.

Experimental study on removing heavy metals from the municipal solid waste incineration fly ash with the modified electrokinetic remediation device.

The MSWI fly ash which contains a large number of heavy metal substances is a subsidiary product of waste incineration power generation technology. If the MSWI fly ash is disposed improperly, heavy metal pollutants will pose a great threat to environmental safety and human health. Based on the technology of electrokinetic remediation, the feasibility of removing heavy metal pollutants from the MSWI fly ash using a modified electrokinetic remediation device - cylinder device was evaluated in this study. Differing from the traditional cuboid device with the volume ratio of the cathode chamber to the anode chamber being 1:1, the volume ratio of the cathode chamber to the anode chamber of the cylinder device was 16:1. Changes in parameters, such as pH values and conductivity in the cathode and the anode chambers as well as current and voltage in the sample area were analysed under the voltage gradient of 2 V/cm. After the experiment, the average removal efficiencies for Zn, Pb, Cd and Cu in the sample area were 53.2%, 31.4%, 42.3% and 30.7%, respectively. It indicates that the cylinder device is effective in removing heavy metals from the MSWI fly ash. Adopting the cylinder device for the experimental study on the electrokinetic remediation technology could provide a better way of thinking for the future engineering practices and applications.

Interferon-gamma induces autophagy-associated apoptosis through induction of cPLA2-dependent mitochondrial ROS generation in colorectal cancer cells.

Colorectal cancer (CRC) is the second most commonly diagnosed cancer in females and the third in males. In this work, we aim to investigate the possible anti-cancer effects of interferon-gamma (IFN-γ) in CRC cells. We observed that IFN-γ induced mitochondria-derived reactive oxygen species (ROS) production in a time-dependent manner in SW480 and HCT116 cell lines. The IFN-γ-induced mitochondrial ROS generation was dependent on the activation of cytosolic phospholipase A2 (cPLA2). In addition, a mitochondria-targeted antioxidant SS31 and/or cPLA2 inhibitor AACOCF3 abolished the IFN-γ-induced ROS production and subsequent autophagy and apoptosis. Moreover, suppression of autophagy by CQ was able to reduce IFN-γ-induced cell apoptosis. Beclin-1 gene silencing resulted in caspase-3 inactivation, decreased Bax/Bcl-2 ratio and less population of apoptotic cells. Collectively, our results suggested that IFN-γ induces autophagy-associated apoptosis in CRC cells via inducing cPLA2-dependent mitochondrial ROS production.