Coal Price Forecasting and Structural Analysis in China

Coal plays an important role in China’s energy structure and its price has been continuously decreasing since the second half of 2012. Constant low price of coal affected the profits of coal enterprises and the coal use of its downstream firms; the precision of coal price provides a reference for these enterprises making their management strategy. Based on the historical data of coal price and related factors such as port stocks, sales volume, futures prices, Producer Price Index (PPI), and crude oil price rate from November 2013 to June 2016, this study aims to forecast coal price using vector autoregression (VAR) model and portray the dynamic correlations between coal price and variables by the impulse response function and variance decomposition. Comparing predicted and actual values, the root mean square error (RMSE) was small which indicated good precision of this model. Thus this short period prediction can help these enterprises make the right business decisions

Coefficient of variation method combined with XGboost ensemble model for wheat growth monitoring

IntroductionObtaining wheat growth information accurately and efficiently is the key to estimating yields and guiding agricultural development.MethodsThis paper takes the precision agriculture demonstration area of Jiaozuo Academy of Agriculture and Forestry in Henan Province as the research area to obtain data on wheat biomass, nitrogen content, chlorophyll content, and leaf area index. By using the coefficient of variation method, a Comprehensive Growth Monitoring Indicator (CGMI) was constructed to perform fractional derivative processing on drone spectral data, and correlation analysis was performed on the fractional derivative spectra with a single indicator and CGMI, respectively. Then, grey correlation analysis was carried out on differential spectral bands with high correlation, the grey correlation coefficients between differential spectral bands were calculated, and spectral bands with high correlation were screened and taken as input variables for the model. Next, ridge regression, random forest, and XGboost models were used to establish a wheat CGMI inversion model, and the coefficient of determination (R2) and root mean squared error (RMSE) were adopted for accuracy evaluation to optimize the wheat optimal growth inversion model.Results and discussionThe results of the study show that: using the data of wheat biomass, nitrogen content, chlorophyll content and leaf area index to construct the comprehensive growth monitoring indicators, the correlation between the wheat growth monitoring indicators and the spectra was calculated, and the results showed that the correlation between the comprehensive growth monitoring indicators and the single indicator correlation had different degrees of increase, and the growth rate could reach 82.22%. The correlation coefficient between the comprehensive growth monitoring indexes and the differential spectra reached 0.92 at the flowering stage, and compared with the correlation coefficient with the original spectra at the same period, the correlation coefficients increased to different degrees, which indicated that the differential processing of spectral data could effectively enhance the spectral correlation. The three models of Random Forest, Ridge Regression and XGBoost were used to construct the wheat growth inversion model with the best effect at the flowering stage, and the XGBoost model had the highest inversion accuracy when comparing in the same period, with the training and test sets reaching 0.904 and 0.870, and the RMSEs were 0.050 and 0.079, so that the XGBoost model can be used as an effective method of monitoring the growth of wheat. To sum up, this study demonstrates that the combination of constructing comprehensive growth monitoring indicators and differential processing spectra can effectively improve the accuracy of wheat growth monitoring, bringing new methods for precision agriculture management

Aperture Modeling Using a Hybrid Method for RFI Analysis

A hybrid method is proposed for radio frequency interference (RFI) prediction of a metal enclosure with an aperture on the top wall. The structure is divided into several segments. While the fields in rectangular segments are described by cavity model, the segments with apertures are modeled by the commercial finite element solver (HFSS). Tangential field continuities along the common boundaries of different segments are enforced by the voltages and currents of boundary ports. Good agreement has been achieved between the hybrid method and full wave simulation

Right upper lobe segmentectomy and subsegmentectomy guided by classification pattern of peripheral segmental veins

BackgroundStudies have analyzed the simplified branching pattern of peripheral segmental veins and developed a standardized approach for intersegmental vein identification in the right upper lobe (RUL). However, the identification approach of intersubsegmental veins has not been reported. This study aimed to supplement the identification approach of intersubsegmental veins and the classification pattern of peripheral segmental veins by using three-dimensional computed tomography bronchography and angiography (3D-CTBA).Materials and methodsA total of 600 patients with ground glass opacity (GGO) who had undergone 3D-CTBA preoperatively at Hebei General Hospital between September 2020 and September 2022 were used for the retrospective study. We reviewed the anatomical variations of RUL veins in these patients using 3D-CTBA images.ResultsAccording to the anatomical position, the peripheral segmental veins structures of RUL were classified into five categories: “Iab type of anterior with central vein” (256/600, 42.7%), “Ib type of anterior with central vein” (166/600, 27.7%), “Central vein type” (38/600, 6.3%), “Anterior vein type” (81/600, 13.5%), “Right top pulmonary vein type” (57/600, 9.5%). The approach for intersegmental vein and intersubsegmental veins identification was divided into five types: anterior approach, posterobronchial approach, central vein approach, V2t approach, and intermediate bronchus posterior surface approach.ConclusionsThe classification pattern of peripheral segmental veins should find wide application. Further, approaches identifying intersegmental veins and intersubsegmental veins may help thoracic surgeons perform safe and accurate RUL segmentectomy

Label-free LC-MS/MS proteomics analyses reveal CLIC1 as a predictive biomarker for bladder cancer staging and prognosis

IntroductionBladder cancer (BC) is a significant carcinoma of the urinary system that has a high incidence of morbidity and death owing to the challenges in accurately identifying people with early-stage BC and the lack of effective treatment options for those with advanced BC. Thus, there is a need to define new markers of prognosis and prediction.MethodsIn this study, we have performed a comprehensive proteomics experiment by label-free quantitative proteomics to compare the proteome changes in the serum of normal people and bladder cancer patients—the successful quantification of 2064 Quantifiable proteins in total. A quantitative analysis was conducted to determine the extent of changes in protein species' relative intensity and reproducibility. There were 43 upregulated proteins and 36 downregulated proteins discovered in non-muscle invasive bladder cancer and normal individuals. Sixty-four of these proteins were elevated, and 51 were downregulated in muscle-invasive and non-muscle-invasive bladder cancer, respectively. Functional roles of differentially expressed proteins were annotated using Gene Ontology (GO) and Clusters of Orthologous Groups of Proteins (COG). To analyze the functions and pathways enriched by differentially expressed proteins, GO enrichment analysis, protein domain analysis, and KEGG pathway analysis were performed. The proteome differences were examined and visualized using radar plots, heat maps, bubble plots, and Venn diagrams.ResultsAs a result of combining the Venn diagram with protein-protein interactions (PPIs), Chloride intracellular channel 1 (CLIC1) was identified as the primary protein. Using the Gene Set Cancer Analysis (GSCA) website, the influence of CLIC1 on immune infiltration was analyzed. A negative correlation between CD8 naive and CLIC1 levels was found. For validation, immunohistochemical (IHC), qPCR, and western blotting (WB) were performed.Further, we found that CLIC1 was associated with a poor prognosis of bladder cancer in survival analysis.DiscussionOur research screened CLIC1 as a tumor-promoting protein in bladder cancer for the first time using serum mass spectrometry. And CLIC1 associated with tumor stage, and immune infiltrate. The prognostic biomarker and therapeutic target CLIC1 may be new for bladder cancer patients

High-throughput Sequencing Analysis of Diversity and Spatial Heterogeneity of Fungal Community in Pit Muds of Different Ages for Baijiu Production

The fungal community structure, the relationship between fungal flora and physicochemical factors, and the prediction of fungal function in pit muds from different spatial positions of 10- and 50-year-old cellars at Jinhui liquor Co. Ltd. were studied by using Illumina NovaSeq high-throughput sequencing, redundancy analysis and Fungi Functional Guild (FUNGuild). The results showed that the fungal diversity and richness of the 10-year-old pit mud decreased with increasing depth; the fungal diversity of the 50-year-old pit mud showed an overall increasing trend, while the fungal richness initially decrease and then increased. Moreover, for the 10-year-old pit, the fungal diversity and richness of the upper layer of the pit wall were significantly higher than those of the other positions (P < 0.05), while for the 50-year-old cellar, the fungal diversity and richness of the bottom layer were significantly higher than those of the other locations (P < 0.05). The fungal diversity and richness were significantly higher in the wall of the 10-year-old cellar than the 50-year-old cellar (P < 0.05), but were significantly higher in the bottom of the 50-year-old cellar than the 10-year-old cellar (P < 0.05). A total of 21 fungal phyla and 520 genera were detected in all pit mud samples, the relative abundance of four dominant phyla (Ascomycota, Basidiomycota, Mortierellomycota and Rozellomycota) and most dominant genera such as Aspergillus and Kazachstania showed significant changes among pit ages and spatial locations (P < 0.05). Fusarium, Aspergillus, Saccharomyces and Monascus were positively correlated with the contents of water, humus, K+ and Ca2+, while Cladosporium and Vishniacozyma were positively correlated with pH. Seven nutritional modes of fungi were observed, mainly including saprophytic and pathological-saprophytic-symbiotic nutritional modes, and four single and seven mixed functional groups were determined. This study provides a theoretical basis for clarifying the structure and spatial distribution of fungal community in Jinhui Baijiu pit mud

Low-mass dark matter search results from full exposure of PandaX-I experiment

We report the results of a weakly-interacting massive particle (WIMP) dark matter search using the full 80.1\;live-day exposure of the first stage of the PandaX experiment (PandaX-I) located in the China Jin-Ping Underground Laboratory. The PandaX-I detector has been optimized for detecting low-mass WIMPs, achieving a photon detection efficiency of 9.6\%. With a fiducial liquid xenon target mass of 54.0\,kg, no significant excess event were found above the expected background. A profile likelihood analysis confirms our earlier finding that the PandaX-I data disfavor all positive low-mass WIMP signals reported in the literature under standard assumptions. A stringent bound on the low mass WIMP is set at WIMP mass below 10\,GeV/c$^2$, demonstrating that liquid xenon detectors can be competitive for low-mass WIMP searches.Comment: v3 as accepted by PRD. Minor update in the text in response to referee comments. Separating Fig. 11(a) and (b) into Fig. 11 and Fig. 12. Legend tweak in Fig. 9(b) and 9(c) as suggested by referee, as well as a missing legend for CRESST-II legend in Fig. 12 (now Fig. 13). Same version as submitted to PR