Study of the Change Laws of Free Radicals and Functional Groups during Coal Oxidation.

In order to study the change laws of free radicals and functional groups during low-temperature coal oxidation, three coal samples with different metamorphic degrees were selected for ESR and FTIR analysis. The results showed that the concentration of free radicals increased as the temperature increased; meanwhile, the types of free radicals changed constantly, and the free radical variation range decreased with the increase in coal metamorphism. The side chains of aliphatic hydrocarbons in coal with a low metamorphic degree decreased by varying amounts in the initial heating stage. The -OH content of bituminous coal and lignite increased first and then decreased, while that in anthracite decreased first and then increased. In the initial oxidation stage, -COOH first increased rapidly, then decreased rapidly, and then increased before finally decreasing. The content of -C=O in bituminous coal and lignite increased in the initial stage of oxidation. Through gray relational analysis, it was found that there was a significant relationship between free radicals and functional groups, and -OH had the strongest correlation with free radicals. This paper provides a theoretical basis for studying the mechanism of functional groups transforming into free radicals in the process of coal spontaneous combustion.

Characterization and Performance Testing of an Intumescent Nanoinhibitor for Inhibiting Coal Spontaneous Combustion.

Considering disadvantages such as the low thermal stability and environmental pollution of existing gel inhibitors, a green and stable intumescent nanoinhibitor (INI) was prepared and tested. First, polyacrylamide (PAM), nano-silica, and intumescent flame retardant (IFR) were selected as raw materials. The INI was prepared by nanoparticle modification and cross-linking polymerization. Then, the structure and physical properties of INI were tested by Fourier transform infrared spectroscopy, scanning electron microscopy, and rheological experiments. Meanwhile, the inhibition performance of INI was studied through thermogravimetric analysis-Fourier transfer infrared spectroscopy (TGA-FTIR) analysis. The results suggest that the nanomodification improved the dispersibility of INI particles. The addition of modified nano-silica (MNS) and IFR enhances the strength of the reticular structure, thereby improving the transport convenience and covering ability of the INI gel. At high temperatures, IFR can generate a porous foamed carbon layer that further coats the coal. After INI inhibition treatment, the characteristic temperature and activation energy of coal were significantly improved, and the production of carbon monoxide and carbon dioxide decreased. Hence, irrespective of physical properties, physical inhibition performance, or chemical inhibition performance, INI performed well. Research results can provide valuable references for the preparation and performance study of a coal spontaneous combustion inhibitor.

Semi-supervised echo state network with temporal-spatial graph regularization for dynamic soft sensor modeling of industrial processes.

Echo state network (ESN) has been successfully applied to industrial soft sensor field because of its strong nonlinear and dynamic modeling capability. Nevertheless, the traditional ESN is intrinsically a supervised learning technique, which only depends on labeled samples, but omits a large number of unlabeled samples. In order to eliminate this limitation, this work proposes a semi-supervised ESN method assisted by a temporal-spatial graph regularization (TSG-SSESN) for constructing soft sensor model with all the available samples. Firstly, the traditional supervised ESN is enhanced to construct the semi-supervised ESN (SSESN) model by integrating both unlabeled and labeled samples in the reservoir computing procedure. The SSESN computes the reservoir states under high sampling rate for better process dynamic information mining. Furthermore, the SSESN's output optimization objective is modified by applying the local adjacency graph of all training samples as a regularization term. Especially, in view of the dynamic data characteristic, a temporal-spatial graph is constructed by considering both the temporal relationship and the spatial distances. The applications to a debutanizer column process and a wastewater treatment plant demonstrate that the TSG-SSESN model can build much smoother model and has better generalization capability than the basic ESN models in terms of soft sensor prediction results.

Influence of Temperature Change on the Change Law of Free Radicals in Coal.

This study investigates changes in the concentration and types of free radicals in the process of coal heating, first rising and then falling. Hailar lignite, Panjiang bituminous coal, and Yangquan anthracite were selected as coal test samples. The results show that the lignite's concentration of free radical changes during heating is greater than that of bituminous coal or anthracite. It clearly shows that lignite is more prone to spontaneous combustion. In the heating and cooling portion of the experiment, the concentration of free radicals during the cooling process was much more than that of free radicals at the same temperature during the heating process. These results obtained from this research study can provide a reference for the prevention and control of the spontaneous combustion of coal with changes in temperature. This study provides a theoretical basis for the prevention and control of spontaneous combustion of coal and the selection of retarding agents and methods in the process of flame retarding by testing the free radical changes of coal at different temperatures. Also, it provides a reference for preventing and controlling coal oxidation with the change in temperature, first rising and then falling.

Feature rearrangement based deep learning system for predicting heart failure mortality.

BACKGROUND AND OBJECTIVE: Heart Failure is a clinical syndrome commonly caused by any structural or functional impairment. Fast and accurate mortality prediction for Heart Failure is essential to improve the health care of patients and prevent them from death. However, due to the imbalance problem and poor feature representation in Heart Failure data, mortality prediction of Heart Failure is difficult with some simple models. To handle these problems, this study is focused on proposing a fast and accurate Heart Failure mortality prediction framework. METHODS: This paper proposes a feature rearrangement based deep learning system for heart failure mortality prediction. The proposed framework improves the performance of predicting heart failure mortality by handling imbalance problem and achieving better feature representation. This paper also proposes a method named Feature rearrangement based convolutional layer, which demonstrates that the order of the input features is essential for the convolutional network. RESULTS: The proposed system is experimentally evaluated on real-world Heart Failure data collected from the EHR system of Shanghai Shuguang Hospital, where 10,198 in-patients records are extracted between March 2009 and April 2016. Internal comparison results illustrate that the proposed framework achieves the best performance for Heart Failure mortality prediction. Extensive experimental results compared with other machine learning methods demonstrate that the proposed method has the highest average accuracy and area under the curve while predicting the three goals of in-hospital mortality, 30-day mortality, and 1-year mortality. Finally, top 12 essential clinical features are mined with their chi-square scores, which can help to assist clinicians in the treatment and research of heart failure. CONCLUSIONS: The proposed method successfully predict different target in three observation windows. Feature rearrangement based convolutional layer and Focal loss are employed into the proposed framework, which helps promote the prediction accuracy of Heart Failure death. The proposed method is fast and accurate for predicting heart failure mortality, especially for imbalance situation. This paper also provide a reasonable pipeline to model EHRs data and handle imbalance problem in medical data.

Weight-based multiple empirical kernel learning with neighbor discriminant constraint for heart failure mortality prediction.

Heart Failure (HF) is one of the most common causes of hospitalization and is burdened by short-term (in-hospital) and long-term (6-12 month) mortality. Accurate prediction of HF mortality plays a critical role in evaluating early treatment effects. However, due to the lack of a simple and effective prediction model, mortality prediction of HF is difficult, resulting in a low rate of control. To handle this issue, we propose a Weight-based Multiple Empirical Kernel Learning with Neighbor Discriminant Constraint (WMEKL-NDC) method for HF mortality prediction. In our method, feature selection by calculating the F-value of each feature is first performed to identify the crucial clinical features. Then, different weights are assigned to each empirical kernel space according to the centered kernel alignment criterion. To make use of the discriminant information of samples, neighbor discriminant constraint is finally integrated into multiple empirical kernel learning framework. Extensive experiments were performed on a real clinical dataset containing 10, 198 in-patients records collected from Shanghai Shuguang Hospital in March 2009 and April 2016. Experimental results demonstrate that our proposed WMEKL-NDC method achieves a highly competitive performance for HF mortality prediction of in-hospital, 30-day and 1-year. Compared with the state-of-the-art multiple kernel learning and baseline algorithms, our proposed WMEKL-NDC is more accurate on mortality prediction Moreover, top 10 crucial clinical features are identified together with their meanings, which are very useful to assist clinicians in the treatment of HF disease.

Multi-view ensemble learning with empirical kernel for heart failure mortality prediction.

Heart failure (HF) refers to the heart's inability to pump sufficient blood to maintain the body's needs, which has a very serious impact on human health. In recent years, the prevalence of HF has remained high. This paper proposes a multi-view ensemble learning algorithm based on empirical kernel mapping called MVE-EK, which predicts the mortality of patient through hospital records. Multi-view ensemble learning can take advantage of the consistency and complementarity of different views. The MVE-EK first divides the patient's features into multiple views and then divides the samples of each view to multiple subsets through under sampling, which can reduce the imbalance rate of the original dataset and obtain some relatively balanced subsets. Each subset is mapped into kernel space by empirical kernel mapping, which can map samples from linearly inseparable spaces to linearly separable spaces. Finally, the multi-view ensemble learning is performed by the designed loss of acquaintance between views. The effectiveness of the algorithm is verified on the three datasets of HF patient in the real world. The performance of the algorithm is better than other comparison algorithms. The datasets are collected from Shanghai Shuguang Hospital and involve 10 203 hospitalization records for 4682 HF patients between March 2009 and April 2016. The prediction information provided by the algorithm can assist the clinician in providing a more personalized treatment plan for patients with HF.

Magnetically Aligned Co-C/MWCNTs Composite Derived from MWCNT-Interconnected Zeolitic Imidazolate Frameworks for a Lightweight and Highly Efficient Electromagnetic Wave Absorber.

Developing lightweight and highly efficient electromagnetic wave (EMW) absorbing materials is crucial but challenging for anti-electromagnetic irradiation and interference. Herein, we used multiwalled carbon nanotubes (MWCNTs) as templates for growth of Co-based zeolitic imidazolate frameworks (ZIFs) and obtained a Co-C/MWCNTs composite by postpyrolysis. The MWCNTs interconnected the ZIF-derived Co-C porous particles, constructing a conductive network for electron hopping and migration. Moreover, the Co-C/MWCNTs composite was aligned in paraffin matrix under an external magnetic field, which led to a stretch of the MWCNTs along the magnetic field direction. Due to the anisotropic permittivity of MWCNTs, the magnetic alignment considerably increased the dielectric loss of the Co-C/MWCNTs composite. Benefiting from the conductive network, the orientation-enhanced dielectric loss, and the synergistic effect between magnetic and dielectric components, the magnetically aligned Co-C/MWCNTs composite exhibited extremely strong EMW absorption, with a minimum reflection loss (RL) of -48.9 dB at a filler loading as low as 15 wt %. The specific RL value (RL/filler loading) of the composite was superior to that of the previous MOF-derived composite absorbers. It is expected that the proposed strategy can be extended to the fabrication of other lightweight and high-performance EMW-absorbing materials.

An automatic approach for constructing a knowledge base of symptoms in Chinese.

BACKGROUND: While a large number of well-known knowledge bases (KBs) in life science have been published as Linked Open Data, there are few KBs in Chinese. However, KBs in Chinese are necessary when we want to automatically process and analyze electronic medical records (EMRs) in Chinese. Of all, the symptom KB in Chinese is the most seriously in need, since symptoms are the starting point of clinical diagnosis. RESULTS: We publish a public KB of symptoms in Chinese, including symptoms, departments, diseases, medicines, and examinations as well as relations between symptoms and the above related entities. To the best of our knowledge, there is no such KB focusing on symptoms in Chinese, and the KB is an important supplement to existing medical resources. Our KB is constructed by fusing data automatically extracted from eight mainstream healthcare websites, three Chinese encyclopedia sites, and symptoms extracted from a larger number of EMRs as supplements. METHODS: Firstly, we design data schema manually by reference to the Unified Medical Language System (UMLS). Secondly, we extract entities from eight mainstream healthcare websites, which are fed as seeds to train a multi-class classifier and classify entities from encyclopedia sites and train a Conditional Random Field (CRF) model to extract symptoms from EMRs. Thirdly, we fuse data to solve the large-scale duplication between different data sources according to entity type alignment, entity mapping, and attribute mapping. Finally, we link our KB to UMLS to investigate similarities and differences between symptoms in Chinese and English. CONCLUSIONS: As a result, the KB has more than 26,000 distinct symptoms in Chinese including 3968 symptoms in traditional Chinese medicine and 1029 synonym pairs for symptoms. The KB also includes concepts such as diseases and medicines as well as relations between symptoms and the above related entities. We also link our KB to the Unified Medical Language System and analyze the differences between symptoms in the two KBs. We released the KB as Linked Open Data and a demo at https://datahub.io/dataset/symptoms-in-chinese .

Porous CNTs/Co Composite Derived from Zeolitic Imidazolate Framework: A Lightweight, Ultrathin, and Highly Efficient Electromagnetic Wave Absorber.

Porous carbon nanotubes/cobalt nanoparticles (CNTs/Co) composite with dodecahedron morphology was synthesized by in situ pyrolysis of the Co-based zeolitic imidazolate framework in a reducing atmosphere. The morphology and microstructure of the composite can be well tuned by controlling the pyrolysis conditions. At lower pyrolysis temperature, the CNTs/Co composite is composed of well-dispersed Co nanoparticles and short CNT clusters with low graphitic degree. The increase of pyrolysis temperature/time promotes the growth and graphitization of CNTs and leads to the aggregation of Co nanoparticles. The optimized CNTs/Co composite exhibits strong dielectric and magnetic losses as well as a good impedance matching property. Interestingly, the CNTs/Co composite displays extremely strong electromagnetic wave absorption with a maximum reflection loss of -60.4 dB. More importantly, the matching thickness of the absorber is as thin as 1.81 mm, and the filler loading of composite in the matrix is only 20 wt %. The highly efficient absorption is closely related to the well-designed structure and the synergistic effect between CNTs and Co nanoparticles. The excellent absorbing performance together with lightweight and ultrathin thickness endows the CNTs/Co composite with the potential for application in the electromagnetic wave absorbing field.

Enhanced high-frequency absorption of anisotropic Fe3O4/graphene nanocomposites.

Anisotropic Fe3O4 nanoparticle and a series of its graphene composites have been successfully prepared as high-frequency absorbers. The crystal structure, morphology and magnetic property of the samples were detailed characterized through X-ray diffractometer (XRD), transmission electron microscopy (TEM) and vibrating sample magnetometer (VSM). The high-frequency absorbing performance of the composites is evaluated within 2.0-18.0 GHz. Combining reduced graphene oxide (RGO) to Fe3O4 helps to adjust the permittivity and permeability of the composite, balance the dielectric loss and magnetic loss, consequently improve the absorbing performance in view of the impedance matching characteristic. The optimal reflection loss of the pure Fe3O4 sample reaches -38.1 dB with a thickness of 1.7 mm, and it increases to -65.1 dB for the sample grafted with 3 wt.% RGO. The addition of proper content of RGO both improves the reflection loss and expands the absorbing bandwidth. This work not only opens a new method and an idea for tuning the electromagnetic properties and enhancing the capacity of high-efficient absorbers, but also broadens the application of such kinds of lightweight absorbing materials frameworks.

Synthesis of flower-like BaTiO3/Fe3O4 hierarchically structured particles and their electrorheological and magnetic properties.

Flower-like BaTiO3/Fe3O4 hierarchically structured particles composed of nano-scale structures on micro-scale materials were synthesized by a simple solvothermal approach and characterized by the means of X-ray powder diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), magnetic testing and rotary viscometer. The influences on the morphology and structure of solvothermal times, type and amount of surfactant, EG : H2O ratio, etc. were studied. Magnetic testing results show that the samples have strong magnetism and they exhibit superparamagnetic behavior, as evidenced by no coercivity and the remanence at room temperature, due to their very small sizes, observed on the M-H loop. The saturation magnetization (M(s)) value can achieve 18.3 emu g(-1). The electrorheological (ER) effect was investigated using a suspension of the flower-like BaTiO3/Fe3O4 hierarchically structured particles dispersed in silicone oil. We can observe a slight shear-thinning behavior of shear viscosity at a low shear rate region even at zero applied electric field and a Newtonian fluid behavior at high shear rate regions.

The synthesis and properties of bifunctional and intelligent Fe3O4@titanium oxide core/shell nanoparticles.

A simple, one-pot solvothermal method has been demonstrated for the preparation of bifunctional Fe3O4@titanium oxide core/shell nanoparticles. In a typical procedure, tetraalkoxyl titanium Ti(OC4H9)4 and FeCl3 as precursors were added into ethylene glycol and further solvothermal treatment was used to synthesize the core/shell particles. The core/shell particles were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), SQUID MPMS and rheometry. The morphological results showed titanium oxide nanorods with 100-200 nm length and 10-20 nm diameter coated on the surface of 200-300 nm Fe3O4 submicrospheres. Reaction time, the titanium source, the barium salt etc. have an influence on the morphology of core/shell particles. The core/shell particles can not only respond to an external magnetic field, but also to an electric field--a novel application of electrorheological fluid.