Investigation of Aluminate Coupling Agent as Modifier and Its Application on Improving Flame Retardant of Intumescent Flame Retardant Coatings

Haoran Jiang,Yong Jiang,Xianli Zhu,Yachao Wang 한국고분자학회 2020 Macromolecular Research Vol.28 No.13

Intumescent flame retardant coating modified by aluminate coupling agent were prepared via sol-gel method using ammonium polyphosphate, polyacrylamide, aluminate coupling agent (ACA), and boron nitride (BN). The effect of modification of ACA on its flame retardant and characterization were preliminarily studied by cone calorimeter, scanning electron microscope, surface tensiometer and contact angle measurement. It is found that the addition of ACA can increase the expansion rate of carbon layer, and a stronger blocking effect can lead to a better property of flame retardant. The sample containing BN, after being modified by 3M NaOH and ACA, dramatically decreases heat release rate, especially reduce the peak heat release rate (pHRR) by 54% and total heat release by 16%, respectively. Moreover, the time to ignition was delayed by 175%. Through examining the residues and pyrolysis products of the coating samples, the mechanism of enhanced flame retardant that promotes the char-forming and heat barrier of the modification of ACA and BN is proposed. This work can offer a significant guide for further studying of ACA and modified BN on the flame retardant.

Load Forecasting Based on LSTM Neural Network and Applicable to Loads of “Replacement of Coal with Electricity”

Chen Zexi,Zhang Delong,Jiang Haoran,Wang Longze,Chen Yongcong,Xiao Yang,Liu Jinxin,Zhang Yan,Li Meicheng 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.5

With the complete implementation of the “Replacement of Coal with Electricity” policy, electric loads borne by urban power systems have achieved explosive growth. The traditional load forecasting method based on “similar days” only applies to the power systems with stable load levels and fails to show adequate accuracy. Therefore, a novel load forecasting approach based on long short-term memory (LSTM) was proposed in this paper. The structure of LSTM and the procedure are introduced firstly. The following factors have been fully considered in this model: time-series characteristics of electric loads; weather, temperature, and wind force. In addition, an experimental verification was performed for “Replacement of Coal with Electricity” data. The accuracy of load forecasting was elevated from 83.2 to 95%. The results indicate that the model promptly and accurately reveals the load capacity of grid power systems in the real application, which has proved instrumental to early warning and emergency management of power system faults. With the complete implementation of the “Replacement of Coal with Electricity” policy, electric loads borne by urban power systems have achieved explosive growth. The traditional load forecasting method based on “similar days” only applies to the power systems with stable load levels and fails to show adequate accuracy. Therefore, a novel load forecasting approach based on long short-term memory (LSTM) was proposed in this paper. The structure of LSTM and the procedure are introduced fi rstly. The following factors have been fully considered in this model: time-series characteristics of electric loads; weather, temperature, and wind force. In addition, an experimental verifi cation was performed for “Replacement of Coal with Electricity” data. The accuracy of load forecasting was elevated from 83.2 to 95%. The results indicate that the model promptly and accurately reveals the load capacity of grid power systems in the real application, which has proved instrumental to early warning and emergency management of power system faults.

Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning

Walsh-Messinger, Julie,Jiang, Haoran,Lee, Hyejoo,Rothman, Karen,Ahn, Hongshik,Malaspina, Dolores Elsevier/North Holland Biomedical Press 2019 Psychiatry Research Vol. No.

<P><B>Abstract</B></P> <P>This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases with schizophrenia (<I>n</I> = 60), schizoaffective disorder (<I>n</I> = 19), bipolar disorder (<I>n</I> = 20), unipolar depression (<I>n</I> = 14), and healthy controls (<I>n</I> = 51) into psychiatric diagnostic categories. The Random Forest machine learning algorithm, which showed best efficacy (92.9% SD: 0.06), was used to generate variable importance ranking of positive, negative, and general psychopathology symptoms, cognitive indexes, global assessment of function (GAF), and parental ages at birth for sorting participants into diagnostic categories. Symptoms were ranked most influential for separating cases from healthy controls, followed by cognition and maternal age. To separate schizophrenia/schizoaffective disorder from bipolar/unipolar depression, GAF was most influential, followed by cognition and paternal age. For classifying schizophrenia from all other psychiatric disorders, low GAF and paternal age were similarly important, followed by cognition, psychopathology and maternal age. Controls misclassified as schizophrenia cases showed lower nonverbal abilities, mild negative and general psychopathology symptoms, and younger maternal or older paternal age. The importance of symptoms for classification of cases and lower GAF for diagnosing schizophrenia, notably more important and distinct from cognition and symptoms, concurs with current practices. The high importance of parental ages is noteworthy and merits further study.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Machine-learning algorithms estimated importance of multilevel data for diagnostic classification. </LI> <LI> Symptoms were most influential for differentiating psychiatric cases from healthy controls. </LI> <LI> Function was most important for separating the schizophrenias from affective disorder cases. </LI> <LI> Function and paternal age were equally important for separating schizophrenia from all other cases. </LI> <LI> Misclassified controls had mild symptoms, lower cognition, and/or younger mothers/older fathers. </LI> </UL> </P>

Influence of Li Addition on the Microstructures and Mechanical Properties of Mg–Li Alloys

Jun Zhao,Jie Fu,Bin Jiang,Aitao Tang,Haoran Sheng,Tianhao Yang,Guangsheng Huang,Dingfei Zhang,Fusheng Pan 대한금속·재료학회 2021 METALS AND MATERIALS International Vol.27 No.6

In this study, Mg-xLi (x = 0, 1, 2, 3 and 5 wt%) alloys have been extruded to examine the role of Li content on microstructuresand tensile properties. The results revealed that Li addition increased the grain size and led to the formation of the transversedirection (TD)-split texture. These were mainly attributed to the promoted DRX process and the increased activity of prismatic⟨a⟩ slip during extrusion. Tensile tests revealed that the elongation of Mg-5Li sheet reached ~ 22.4% along the ED. Moreover, it exhibited a higher elongation of ~ 27.3%, three times than pure Mg, along the TD. During tension along the ED,with increasing Li content, more prismatic ⟨a⟩ slip and preferable intergranular strain coordination ability to accommodatethe plastic strain, leading to the enhanced room-temperature ductility. In contrast, more basal ⟨a⟩ slips and extension twinswere also activated along the TD, which further contributed to the enhanced ductility. Therefore, the ductility of Mg sheetsat room temperature gradually improved with Li addition due to the combining effects of basal ⟨a⟩ slip, prismatic ⟨a⟩ slip,extension twin and preferable intergranular strain coordination ability.

Exosomes derived from miR-214-3p overexpressing mesenchymal stem cells promote myocardial repair

Wenwu Zhu,Qingjie Wang,Jian Zhang,Ling Sun,Xiu Hong,Wei Du,Rui Duan,Jianguang Jiang,Yuan Ji,Haoran Wang,Bing Han 한국생체재료학회 2023 생체재료학회지 Vol.27 No.00

Aims Exosomes are known as nanovesicles that are naturally secreted, playing an essential role in stem-mediated cardioprotection. This study mainly focused on investigating if exosomes derived from miR-214 overexpressing mesenchymal stem cells (MSCs) show more valid cardioprotective ability in a rat model of acute myocardial infarction (AMI) and its potential mechanisms. Methods Exosomes were isolated from control MSCs (Ctrl-Exo) and miR-214 overexpressing MSCs (miR-214OE-Exo) and then they were delivered to cardiomyocytes and endothelial cells in vitro under hypoxia and serum deprivation (H/SD) condition or in vivo in an acutely infarcted Sprague-Dawley rat heart. Regulated genes and signal pathways by miR-214OE-Exo treatment were explored using western blot analysis and luciferase assay. Results in vitro , miR-214OE-Exo enhanced migration, tube-like formation in endothelial cells. In addition, miR-214OE-Exo ameliorated the survival of cardiomyocytes under H/SD. In the rat AMI model, compared to Ctrl-Exo, miR-214OE-Exo reduced myocardial apoptosis, and therefore reduced infarct size and improved cardiac function. Besides, miR-214OE-Exo accelerated angiogenesis in peri-infarct region. Mechanistically, we identified that exosomal miR-214-3p promoted cardiac repair via targeting PTEN and activating p-AKT signal pathway. Conclusion Exosomes derived from miR-214 overexpressing MSCs have greatly strengthened the therapeutic efficacy for treatment of AMI by promoting cardiomyocyte survival and endothelial cell function.