Temperature-dependent decomposition of the CL-20/MTNP cocrystal after phase separation.

CL-20 (2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane)-based cocrystals are attractive energetic cocrystals with a potential for high energy and low sensitivity, which account for nearly one-third of energetic cocrystals. The applications of cocrystal explosives require in-depth understanding of their thermal kinetics behaviors. Although thermal kinetics of the decomposition of CL-20-based cocrystals having no melting point have been studied, relevant research of CL-20-based cocrystals having a melting point, which are also the most frequently observed type, is still rare. In this study, the CL-20/MTNP (1-methyl-3,4,5-trinitropyrazole) cocrystal was chosen as a typical CL-20-based cocrystal having a melting point to investigate its thermal kinetics behavior. The thermal decomposition of CL-20/MTNP was identified to be a typical heterogeneous reaction with phase separation before decomposition. Due to the presence of intermolecular hydrogen bonds between CL-20 and molten MTNP after phase separation, the thermal decomposition behavior of CL-20/MTNP was strongly temperature-dependent. The complex decomposition reaction was separated into its three constituent pathways to simplify the kinetic analysis. On the basis of in-depth understanding of the decomposition process, the best functions of mechanism and kinetic parameters for each process of CL-20/MTNP decomposition were obtained using the model-fitting method. Finally, important thermal safety indicators, such as TMRad and SADT were simulated by combining the established kinetic models. This study provides further insights into the entire reaction process of the CL-20/MTNP cocrystal and would help in its better applications.

Prediction of road dust concentration in open-pit coal mines based on multivariate mixed model.

The problem of dust pollution in the open-pit coal mine significantly impacts the health of staff, the regular operation of mining work, and the surrounding environment. At the same time, the open-pit road is the largest dust source. Therefore, it analyzes the influencing factors of road dust concentration in the open-pit coal mine. It is of practical significance to establish a prediction model for scientific and effective prediction of road dust concentration in the open pit coal mine. The prediction model helps reduce dust hazards. This paper uses the hourly air quality and meteorological data of an open-pit coal mine in Tongliao City, Inner Mongolia Autonomous Region, from January 1, 2020, to December 31, 2021. Create a CNN-BiLSTM-Attention multivariate hybrid model consisting of a Convolutional Neural Network (CNN), a bidirectional long short-term memory neural network (BiLSTM), and an attention mechanism, Prediction of PM2.5 concentration in the next 24h. Establish prediction models of parallel and serial structures, and carry out many experiments according to the change period of the data to determine the optimal configuration and the input and output size. Then, a comparison of the proposed model and Lasso regression, SVR, XGBoost, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM models for short-term prediction (24h) and long-term prediction (48h, 72h, 96h, and 120h). The results show that the CNN-BiLSTM-Attention multivariate mixed model proposed in this paper has the best prediction performance. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) of the short-term forecast (24h) are 6.957, 8.985, and 0.914, respectively. Evaluation indicators of long-term forecasts (48h, 72h, 96h, and 120h) are also superior to contrast models. Finally, we used field-measured data to verify, and the obtained evaluation indexes MAE, RMSE, and R2 are 3.127, 3.989, and 0.951, respectively. The model-fitting effect was good.

Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model.

As an equipment failure that often occurs in coal production and transportation, belt conveyor failure usually requires many human and material resources to be identified and diagnosed. Therefore, it is urgent to improve the efficiency of fault identification, and this paper combines the internet of things (IoT) platform and the Light Gradient Boosting Machine (LGBM) model to establish a fault diagnosis system for the belt conveyor. Firstly, selecting and installing sensors for the belt conveyor to collect the running data. Secondly, connecting the sensor and the Aprus adapter and configuring the script language on the client side of the IoT platform. This step enables the collected data to be uploaded to the client side of the IoT platform, where the data can be counted and visualized. Finally, the LGBM model is built to diagnose the conveyor faults, and the evaluation index and K-fold cross-validation prove the model's effectiveness. In addition, after the system was established and debugged, it was applied in practical mine engineering for three months. The field test results show: (1) The client of the IoT can well receive the data uploaded by the sensor and present the data in the form of a graph. (2) The LGBM model has a high accuracy. In the test, the model accurately detected faults, including belt deviation, belt slipping, and belt tearing, which happened twice, two times, one time and one time, respectively, as well as timely gaving warnings to the client and effectively avoiding subsequent accidents. This application shows that the fault diagnosis system of belt conveyors can accurately diagnose and identify belt conveyor failure in the coal production process and improve the intelligent management of coal mines.

"Thermal escape" of MTNP: the phase separation of CL-20/MTNP cocrystals under long-term heating.

High-energy, low-sensitivity energetic cocrystals are one successful application of the supramolecular strategy. The practical application of cocrystal explosives requires an in-depth understanding of the stability of their crystal phase structure under long-term heating, but relevant research is rare. In this study, the CL-20/MTNP (2, 4, 6, 8, 10, 12-hexanitrohexaazaisowurtzitane/1-methyl-3,4,5-trinitropyrazole) cocrystal was selected as a representative cocrystal explosive to investigate its crystal phase structure stability under long-term heating. The phase separation of the CL-20/MTNP cocrystal was observed for the first time. It was revealed that the MTNP molecules at crystal defects first underwent molecular rotation, which weakened interactions between CL-20 and MTNP molecules. Then, the MTNP molecules diffused along channels surrounded by CL-20 molecules to the crystal surface and escaped to generate γ-CL-20. We call this process the "thermal escape" of MTNP, whose effect on the safety performance of the CL-20/MTNP cocrystal was studied by comparing the mechanical sensitivity of samples with different degrees of thermal escape. The mechanical sensitivity of the CL-20/MTNP cocrystal did not greatly change during the induction period, but it increased upon the loss of MTNP. Moreover, the thermal escape kinetics for the two stages were obtained to prevent or control their thermal escape. The prediction of the kinetics confirmed the validity of the kinetic analysis. This study promotes the performance evaluation and application of CL-20/MTNP cocrystals and also provides a new perspective in the investigation of cocrystal explosives.

Design of PM2.5 monitoring and forecasting system for opencast coal mine road based on internet of things and ARIMA Mode.

The dust produced by transportation roads is the primary source of PM2.5 pollution in opencast coal mines. However, China's opencast coal mines lack an efficient and straightforward construction scheme of monitoring and management systems and a short-term prediction model to support dust control. In this study, by establishing a PM2.5 and other real-time environmental information to monitor, manage, visualize and predict the Internet of things monitoring and prediction system to solve these problems. This study solves these problems by establishing an Internet of things monitoring and prediction system, which can monitor PM2.5 and other real-time environmental information for monitoring, management, visualization, and prediction. We use Lua language to write interface protocol code in the APRUS adapter, which can simplify the construction of environmental monitoring system. The Internet of things platform has a custom visualization scheme, which is convenient for managers without programming experience to manage sensors and real-time data. We analyze real-time data using a time series model in Python, and RMSE and MAPE evaluate cross-validation results. The evaluation results show that the average RMSE of the ARIMA (4,1,0) and Double Exponential Smoothing models are 12.68 and 8.34, respectively. Both models have good generalization ability. The average MAPE of the fitting results are 10.5% and 1.7%, respectively, and the relative error is small. Because the ARIMA model has a more flexible prediction range and strong expansibility, and ARIMA model shows good adaptability in cross-validation, the ARIMA model is more suitable as the short-term prediction model of the prediction system. The prediction system can continuously predict PM2.5 dust through the ARIMA model. The monitoring and prediction system is very suitable for managers of opencast coal mines to prevent and control road dust.

Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials.

Cocrystal engineering have been widely applied in pharmaceutical, chemistry and material fields. However, how to effectively choose coformer has been a challenging task on experiments. Here we develop a graph neural network (GNN) based deep learning framework to quickly predict formation of the cocrystal. In order to capture main driving force to crystallization from 6819 positive and 1052 negative samples reported by experiments, a feasible GNN framework is explored to integrate important prior knowledge into end-to-end learning on the molecular graph. The model is strongly validated against seven competitive models and three challenging independent test sets involving pharmaceutical cocrystals, π-π cocrystals and energetic cocrystals, exhibiting superior performance with accuracy higher than 96%, confirming its robustness and generalization. Furthermore, one new energetic cocrystal predicted is successfully synthesized, showcasing high potential of the model in practice. All the data and source codes are available at https://github.com/Saoge123/ccgnet for aiding cocrystal community.

Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability.

Longwall top coal caving technology is one of the main methods of thick coal seam mining in China, and the classification evaluation of top coal cavability in longwall top coal caving working face is of great significance for improving coal recovery. However, the empirical or numerical simulation method currently used to evaluate the top coal cavability has high cost and low-efficiency problems. Therefore, in order to improve the evaluation efficiency and reduce evaluation the cost of top coal cavability, according to the characteristics of classification evaluation of top coal cavability, this paper improved and optimized the fuzzy neural network developed by Nauck and Kruse and establishes the fuzzy neural network prediction model for classification evaluation of top coal cavability. At the same time, in order to ensure that the optimized and improved fuzzy neural network has the ability of global approximation that a neural network should have, its global approximation is verified. Then use the data in the database of published papers from CNKI as sample data to train, verify and test the established fuzzy neural network model. After that, the tested model is applied to the classification evaluation of the top coal cavability in 61,107 longwall top coal caving working face in Liuwan Coal Mine. The final evaluation result is that the top coal cavability grade of the 61,107 longwall top coal caving working face in Liuwan Coal Mine is grade II, consistent with the engineering practice.

Quantitative correlation between facets defects of RDX crystals and their laser sensitivity.

In this work, the {210} facets of cyclotrimethylenetrinitramine (RDX) single crystals with different quality were studied by scanning electron microscopy and atomic force microscopy. Their laser sensitivity was then assessed using a direct laser ignition test irradiated with ultraviolet laser (wavelength: 355nm, pulse width: 6.4ns). Quantitative relationships between laser sensitivity and surface defects of RDX (210) and (2¯1¯0) facets were investigated. It is determined that the laser sensitivity exhibits significant correlation with the surface roughness, size of which is comparable with scales of laser wavelength. 3D FDTD simulations disclose that this relationship can be well explained with light intensity modulation effects induced by micro-defects on the initial plane wave.

Ultraviolet Laser-induced ignition of RDX single crystal.

The RDX single crystals are ignited by ultraviolet laser (355 nm, 6.4 ns) pulses. The laser-induced damage morphology consisted of two distinct regions: a core region of layered fracture and a peripheral region of stripped material surrounding the core. As laser fluence increases, the area of the whole crack region increases all the way, while both the area and depth of the core region increase firstly, and then stay stable over the laser fluence of 12 J/cm(2). The experimental details indicate the dynamics during laser ignition process. Plasma fireball of high temperature and pressure occurs firstly, followed by the micro-explosions on the (210) surface, and finally shock waves propagate through the materials to further strip materials outside and yield in-depth cracks in larger surrounding region. The plasma fireball evolves from isotropic to anisotropic under higher laser fluence resulting in the damage expansion only in lateral direction while maintaining the fixed depth. The primary insights into the interaction dynamics between laser and energetic materials can help developing the superior laser ignition technique.

{µ-N,N,N',N'-Tetra-kis[(diphenyl-phosphan-yl)meth-yl]benzene-1,4-diamine}-bis-[(2,2'-bipyrid-yl)copper(I)] bis-(tetra-fluoridoborate).

In the title compound, [Cu(2)(C(10)H(8)N(2))(2)(C(58)H(52)N(2)P(4))](BF(4))(2), the dinuclear cation lies on an inversion centre. The Cu(I) atom is coordinated by two N atoms from a 2,2'-bipyridine ligand and two P atoms from an N,N,N',N'-tetra-kis-[(diphenyl-phos-phan-yl)meth-yl]benzene-1,4-diamine ligand in a distorted tetra-hedral geometry. In the crystal, inter-molecular C-H⋯F hydrogen bonds link the ions into layers parallel to [[Formula: see text]01]. π-π inter-actions [centroid-centroid distance = 3.668 (4) Å] are also observed. One F atom of the anion is disordered over two orientations with a refined occupancy ratio of 0.675 (13):0.325 (13).

Synthesis and characterization of spherical 2-diazo-4,6-dinitrophenol (DDNP).

Spherical 2-diazo-4,6-dinitrophenol (DDNP) with good flowability and controlled bulk density (0.65-0.95 g/cm(3)) has been prepared at factory scale by the modified method using 4-methylphenol as crystal control ingredient. Results showed that the yield of product was increased by 5-10%, and the waste water was significantly decreased due to circulation use of waste water compared with traditional method. Synthesized spherical DDNP was characterized by IR, laser granularity measurement, SEM, HPLC and XRD. IR spectrum confirmed the structural features of spherical DDNP. The particle analysis revealed that the modified method could offer spherical DDNP with average particle size of 350 microm and high purity (>98.52%). The XRD peaks of spherical DDNP have similar diffraction angles as those of traditional DDNP. The DSC profile of spherical DDNP showed the exothermic decomposition in the temperature range of 161.2-188.5 degrees C. The product can be pressed at over 40MPa without dead pressed phenomenon, and the minimum detonating charge of spherical DDNP was measured to be about 0.15g. Furthermore, impact sensitivity test suggested that spherical DDNP is less sensitive than traditional DDNP.

2-Nitro-p-phenyl-ene dibenzene-sulfonate.

In the title compound, C(18)H(13)NO(8)S(2), the nitro-phenyl ring forms dihedral angles of 46.67 (7) and 75.40 (6)° with the phenyl rings. The nitro group makes a dihedral angle of 26.13 (8)° with the attached ring. The crystal packing is stabilized by weak inter-molecular C-H⋯O hydrogen bonds.

Preventive effect of Qianggan-Rongxian Decoction on rat liver fibrosis.

AIM: To study the preventive effects of Qianggan-Rongxian Decoction on liver fibrosis induced by dimethylnitrosamine (DMN) in rats. METHODS: Male Wistar rats were randomly divided into hepatic fibrosis model group, control group and 3 treatment groups (12 rats in each group). Except for the normal control group, all the rats received 1% DMN (10 muL/kg body weight, i.p), 3 times a week for 4 wk. The rats in the 3 treatment groups including a high-dose DMN group (10 mL/kg), a medium-dose DMN group (7 mL/kg), and a low-dose DMN group (4 mL/kg) were daily gavaged with Qianggan-Rongxian Decoction, and the rats in the model and normal control groups were given saline vehicle. Enzyme-linked immunosorbent assay (ELISA) was used to determine the changes in serum hyaluronic acid (HA), laminin (LN), and type IV collagen levels. Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were measured using routine laboratory methods. Pathologic changes, particularly fibrosis, were examined by hematoxylin and eosin (HE) and Sirius red staining. Hepatic stellate cells (HSC) were examined by transmission electron microscopy. RESULTS: Compared with the model control group, the serum levels of HA, LN, type IV collagen, ALT and AST were decreased markedly in the other groups after treatment with Qianggan-Rongxian Decoction, especially in the medium-dose DMN group (P < 0.05). Moreover, the area-density percentage of collagen fibrosis was lower in the Qianggan-Rongxian Decoction treatment groups than in the model group, and a more significant drop was observed in the medium-dose DMN group (P < 0.05). CONCLUSION: Qianggan-Rongxian Decoction can inhibit hepatic fibrosis due to chronic liver injury, delay the development of cirrhosis, and notably ameliorate liver function. It may be used as a safe and effective thera-peutic drug for patients with fibrosis.

[Epidemiologic study on the relationship between smoking and sleep apnea/hypopnea syndrome].

OBJECTIVE: To investigate how smoking was affecting the prevalence of sleep apnea/ hypopnea syndrome (SAHS) among adults aged over 30 years in Chengde city of Hebei province. METHODS: 1168 subjects, over 30 years of age were derived from a random sample from a community-based population in Shuangqiao district of Chengde city. All subjects responded to a questionnaire at their own houses regarding their habits of snoring and smoking. 1168 subjects (95.2%) answered the questions satisfactorily. RESULTS: (1) Among the smoking groups, the prevalence of snoring was 69.09%, higher than that in the nonsmoking groups 45.07% (P = 0.000). (2) In males, the smoking group had a higher prevalence (69.72%) of snoring than in the nonsmoking group (60.80%, P = 0.033). (3) Females in the smoking group had a higher prevalence of snoring (61.80%) than in the nonsmoking group (39.70%, P = 0.011). (4) The prevalence of snoring in males (60.80%) was significantly higher than that in females (39.70%, P = 0.000). (5) The prevalence (69.72%) of snoring in smoking males was similar to that in smoking females (61.80%, P = 0.336). (6) Data from logistic regression analysis indicated that smoking was one of the factors affecting snoring. (7) According to the degree of snoring, 127 moderate and severe snorers were measured by portable PSG for a whole night and the prevalence of SAHS was estimated. According to the AHI > or = 5 and the ESS > or = 9 cutoff-points, the prevalence rates of SAHS in smoking groups were both significantly higher than that in nonsmoking groups (P < 0.001). CONCLUSION: Smoking and snoring among adults aged over 30 years had correlation in our city.

Relationship between atrial natriuretic peptide-immunoreactive cells and microvessels in rat gastric mucosa.

AIM: To investigate the ultrastructural localization of atrial natriuretic peptide (ANP)-synthesizing cells and the relationship between ANP-synthesizing cells and microvessels in rat gastric mucosa. METHODS: Immunohistochemistry techniques and postembedding immunoelectron microscopy techniques were used to validate the findings regarding the expression of ANP-synthesizing cells and the ultrastructural localization of ANP-synthesizing cells in the gastric mucosa. Histochemistry techniques and the tannic acid-ferric chloride method (TA-Fe staining method) were used to reveal microvessel density and the distribution of ANP-synthesizing cells in different regions of the stomach. RESULTS: Cells expressing ANP were localized and ANP-synthesizing cells were identified as enterochromaffin (EC) cells in the gastric mucosa. ANP-synthesizing cells existed in different regions of the stomach. The percentage ANP-synthesizing cells in the mucosa was greatest in the fundus (46.7%+/-5.3%), intermediate in the antrum (40.1%+/-4.5%), and least in the body (21.6%+/-3.6%). There was a positive relationship between the percentage of ANP-synthesizing cells and the density of microvessels in the antral mucosa, but not in the fundus or body mucosa. CONCLUSION: ANP is synthesized by EC cells in rat gastric mucosa, and ANP-synthesizing cells are most dense in the gastric fundus. ANP may act not only as a regional autocrine and/or paracrine regulator, but also as an endocrine regulatory peptide in the gastrointestinal tract.