Coal mine safety is crucial to the healthy and sustainable development of the coal industry, and coal mine flood is a major hidden danger of coal mine accidents. Therefore, the processing of coal ...mine water source data is of great significance to prevent mine water inrush accidents. In this experiment, the water source data were obtained by laser induced fluorescence technology with the assistance of laser. The water sample data information was preprocessed by standard normal variable transformation (SNV) and multiple scattering correction (MSC), and then the principal component analysis (PCA) was used to reduce the dimension of the data and ensure the information characteristics of the original data unchanged. In order to identify the water inrush type of coal mine water source, the sparrow search algorithm (SSA) is used to optimize the BP neural network in this study. This is because the SSA algorithm has the advantages of strong optimization ability and fast convergence rate compared with particle swarm optimization (PSO) and other optimization algorithms. Experiments show that under the premise of SNV pretreatment, the R 2 of SSA-BP model is infinitely close to 1, MRE is 0.0017, RMSE is 0.0001, the R 2 of PSO-BP model is 0.9995, MRE is 0.0026, RMSE is 0.0019, the R 2 of BP model is 0.9983, MRE is 0.0140, RMSE is 0.0075. Therefore, SSA-BP model is more suitable for the classification of coal mine water sources.
Epstein-Barr virus (EBV) infection is a major global threat; its manifestations range from the absence of symptoms to multiorgan malignancies and various gastrointestinal diseases. Analyzing the ...composition and metabolomic profile of gut microbiota during acute EBV infection might be instrumental in understanding and controlling EBV.
Six tree shrews were inoculated with EBV by intravenous injection. Blood was collected at regular intervals thereafter from the femoral vein to detect EBV and inflammatory biomarker. At the same time, tree shrew faeces were collected for 16 S rRNA gene sequencing and Non-targeted metabolomics analysis.
16 S rRNA gene characterization along with β diversity analysis exhibited remarkable alterations in gut microflora structure with a peak at 7 days post-infection(dpi). Some alterations in the relative richness of bacterial taxon were linked to infectious indicators. Of note, Butyricicoccus relative richness was positively linked to EBV presence in the blood and plasma, the opposite correlation was seen with Variovorax and Paramuribaculum. Non-targeted metabolomics indicated the fecal metabolome profile altered during EBV infection, particularly 7 dpi. The relative abundance of geranic acid and undecylenic acid in stool samples was positively linked to systemic inflammatory biomarkers, and an inverse relationship was reported with the estrone glucuronide, linoleic acid, protoporphyrin IX and tyramine.
Collectively, EBV infection in this model correlated with changes in the composition and metabolome profile of the gut microbiota.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To address the problem of low accuracy of power transformer fault diagnosis, this study proposed a transformer fault diagnosis method based on DBSO-CatBoost model. Based on data feature extraction, ...this method adopted DBSO (Difference-mutation Brain Storm Optimization) algorithm to optimize CatBoost model and diagnose faults. First, for data preprocessing, the ratio method was introduced to add features to the original data, the SHAP (Shapley Additive Explanations) method was applied for feature extraction, and the KPCA (Kernel Principal Component Analysis) algorithm was employed to reduce the dimension of data. Subsequently, the preprocessed data were inputted into the CatBoost model for training, and the DBSO algorithm was adopted to optimize the parameters of the CatBoost model to yield the optimal model. Lastly, the DBSO-CatBoost model was exploited to diagnose the transformer fault and output the fault type. As indicated from the example results, the accuracy of the transformer fault diagnosis based on DBSO-Catboost model could be 93.71%, 3.958% higher than that of CatBoost model and significantly exceeding that of some common models. Furthermore, compared with other preprocessing methods, the accuracy of fault diagnosis by employing the data preprocessing method proposed in this study was significantly improved.
•The highlights of this paper are as follows: Our team use YOLOv5 algorithm to analyze the spectral images of coal gangue, and we have improved the algorithm based on YOLOv5 algorithm. It improves ...the accuracy of classification and recognition of coal gangue, and is of great significance to the separation of coal gangue industry.
Accurate identification of coal-gangue have great significance for separation of coal-gangue. The traditional coal-gangue identification method has the disadvantages of low accuracy and slow speed. Therefore, an intelligent classification method of coal-gangue based on multispectral imaging technology and target detection is proposed in this paper. According to the model structure of YOLOv5, add scSE module in CSPDarknet and CSP module. The improved YOLOv5 is referred to as YOLOv5.1. To begin with, the multispectral data of coal-gangue are collected, and the collected coal-gangue images are screened. Beside, three bands with high recognition rate and correlation are selected from 25 bands to form pseudo-RGB images. Otherwise, the RGB image of coal-gangue was detected by theYOLOv5.1. By detecting the separated single band, the recognition rate and correlation of band 6, 10 and 12 are higher. The experimental results show that the average accuracy of detecting coal-gangue in the test set reaches 98.34 %, and the detection time is about 3.62 s by using the model of YOLOv5.1 to train the RGB image of coal-gangue. This method can not only accurately identify coal-gangue, but also obtain the relative position of coal-gangue, which can be effectively used for coal-gangue identification.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Detection of transformer fault type by LIF technique.•LIF technique can realize on-line detection with simple operation.•DBSO optimization algorithm has high accuracy and less computation.
...Transformer fault diagnosis is an indispensable part of normal operation of power equipment. Aiming at the problem that traditional gas chromatography and other methods take a long time to detect fault types, this paper proposes a laser-induced fluorescence spectroscopy (LIF) technology, combined with differential mutation brainstorm optimization algorithm (DBSO) to optimize the ELM model to identify transformer fault diagnosis types. Four different transformer oils were selected as experimental samples, including thermal fault oil, electrical fault oil, local damp oil and crude oil. LIF technology was used to obtain different spectral images of four oil samples. Firstly, the obtained fluorescence spectra were pre-treated by MSC and Normalize, and the dimensionality was reduced by PLS. Then, the data after dimension reduction are input into the ELM model for training, and the BSO algorithm and DBSO algorithm are used to optimize the parameters of ELM. Finally, the experiments show that the DBSO-ELM model pre-processed by MSC has the highest recognition accuracy, the goodness of fit (R2) is 1, and the mean square error (MSE) reaches 3.205e-31, which is higher than that of ELM model and BSO-ELM model. In the case of the same pre-processing, the fitness values based on the mean square error rate of the training set are lower than those of the other types of recognition algorithm models. Therefore, the MSC-PLS-DBSO-ELM model has the best recognition effect, which can be extended to transformer fault diagnosis and improve the accuracy and safety of power equipment detection.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
To examine the clinical and pathological features, laboratory markers, therapeutic options and risk factors indicating poor prognosis of hydroa vacciniforme-like lymphoproliferative disorder (HVLPD).
...Seven patients with HVLPD had their clinical and pathological data collected. Immunohistochemical staining, Epstein-Barr virus-encoded RNA (EBER) in situ hybridization experiments, T-cell receptor (TCR) gene rearrangement, RT-PCR tests and the Elisa assay were carried out.
The main clinical manifestations were papulovesicular lesions and ulcers on the face, neck, or trunk. Five cases had systemic symptoms. Three of the deceased patients had significant facial edema, deep body necrosis, and ulceration. The pathological results demonstrated that lymphocytes infiltrated blood vessels and sweat glands in addition to the dermis and subcutaneous tissues. All patients tested positive for CD3 and EBER. Six cases tested positive for TCRβF1, but none tested positive for TCRδ. TCRγ monoclonal rearrangement, strongly positive expression of TIA-1 and a Ki67 proliferation index of 40% occurred in 3 fatal cases. When compared to the survival group, the plasma EBV DNA in the deceased group was considerably higher (
<0.05). IFN-γ and TNF-α cytokine levels in patients were higher than in the control group, particularly in the deceased group (
<0.05). The skin lesions on all patients recovered quickly underwent conservative care. Nonetheless, 3 patients passed away as the disease progressed in its latter stages.
In our cases, the main infiltrating cells were T cells and the dominant lymphocyte subclass was αβT cells. A significant increase in lgE level, plasma EBV DNA, IFN-γ, and TNF-α cytokine levels, decreased hemoglobin level, strongly positive expression of TIA-1, high Ki67 proliferation index, and positive TCR gene rearrangement are all indicators of a poor prognosis.
Qualitative analysis and quantitative evaluation are the main methods for demonstration effect evaluation. The essay takes L·A·Zadeh's fuzzy comprehensive evaluation theory as the basis and the ...demonstration effect evaluation indicator system for the Balinzuoqi Rural House Energy-saving Renovation Demonstration project as the object to build the demonstration effect fuzzy comprehensive evaluation model for the rural house energy-saving renovation demonstration project in light of the diversity and fuzziness of the indicator system. It made use of the model to evaluate the demonstration effects of Balinzuoqi Demonstration project and demonstrate the feasibility of the model built through operation of the fuzzy relation of the evaluation indicators. Building the demonstration effect fuzzy comprehensive evaluation model largely depends on the distribution of every evaluation indicator weight and determination of the grade of membership of the single factor indicator to the comment set. This is the main basis to quantify the stationary indicators and also the basis to ensure fuzzy comprehensive evaluation model accuracy and objectiveness of the evaluation results.
Gyilung Gully was our destination, located over 1,000 kilometres to the west of Lhasa and 100 kilometres from the Xixabangma Mountain. The name Gyilung means "gully filled with happiness and ...coziness." Living at the mercy of the weather, they resort to slash and burn farming. In the prolonged process of contact with the local Tibetans, the Dama people have inherited some habits from them: They eat tsamba (roasted highland barley), wear Tibetan robes and speak Tibetan, although they still use their old language which has no written script. In addition, they go to the monastery on the eighth and 15th of each month. Dama people also showed great interest in the construction of the Qinghai-Tibet Railway, where the second-phase of construction will link Lhasa with Golmud of Qinghai Province, across the vast Qinghai- Tibet Plateau, in 2007.
How to solve the agglomeration of graphene in the plating bath is an essential challenge to synthesize graphene reinforced composite coatings by electrodeposition. In this paper, the exfoliated ...graphene (EG) from the graphite electrode was electrodeposited into the composite coating by simultaneously exfoliation and deposition in a ternary deep eutectic solvent. The preparation method is not only simple, but also effective in, and reducing the agglomeration of graphene. The collected EG was characterized by TEM, SEM, and Raman. The results show that few-layers EG is transparent. XRD, SEM, and EDS were used to examine the nickel-graphene composite coating. The results show that the graphene uniformly disperses in the composite coating. The Ni grains are refined, and the crystal plane orientation does not change. Compared with the pure Ni coating, the hardness and wear resistance of the Ni-graphene coating are significantly improved.
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•Exfoliation and deposition of graphene performed by a one-step process•The graphene obtained from graphite anode was co-deposited with Ni ion.•Deposition function of the eutectic solvent is integrated with stripping.•The nickel-graphene coating with excellent tribological properties was obtained.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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