Chengde Area Research Articles

Nakazawaea tricholomae f.a., sp. nov., a Novel Ascomycetous Yeast Species Isolated from Two Mushroom Species in China.

Two yeast strains designated as 20-27-1 and 20-28 were isolated from the fruiting bodies of Tricholoma gambosum and Marasmius maximus, respectively, which were collected in Wudaogou, Weichang county, Chengde area, Hebei Province, China. The multi-locus analysis of the sequences of the rDNA ITS, D1/D2 LSU, and SSU regions, together with partial sequences of two protein-coding genes RPB1 and TEF1 indicates that the two strains are closely related to Nakazawaea ernobii and Nakazawaea holstii, showing the similarity values of 99.3-98.7%, 97.2-97.1%, 91.9-92.5%, and 84.6% in D1/D2 LSU, ITS, TEF1, and RPB1, respectively. Physiologically, the two strains are different from N. ernobii and N. holstii in the assimilation of melibiose, inulin, and DL-lactic acid. Both the phenotypic and phylogenetic analyses indicate that those two strains represent a novel species in the genus Nakazawaea, for which the name Nakazawaea tricholomae f.a., sp. nov. (Fungal Names: FN 571492) is proposed.

Graph inference algorithms as high-performance tools for detecting geochemical anomalies related to mineralization from geochemical exploration data

Geochemical exploration data usually consists of a small number of labeled data (cells containing known deposits) and a large number of unlabeled data (cells containing none of known deposits). How to make full use of a small number of labeled data and a large number of unlabeled data to build a high-performance anomaly detection model for identifying mineralization anomalies from geochemical exploration data is a challenge faced by geochemical exploration practitioners. The graph inference algorithms including label propagation and label spreading can efficiently build high-performance semi-supervised classification models by learning from sparse labeled and abundant unlabeled data in machine learning. For this reason, the two graph inference algorithms were used to build the semi-supervised classification models to detect Fe-mineralization anomalies from stream sediment survey data of 1: 50,000 scale in the Chengde area (Hebei Province, China). The label propagation model and label spreading model were compared with the Gaussian mixture model and logistic regression model in detecting Fe-mineralization anomalies. The data processing time of the label propagation algorithm and label spreading algorithm is 64.26 s and 28.21 s, respectively; and the data processing time of the Gaussian mixture model and logistic regression algorithm is 37.79 s and 18.98 s, respectively. The area under the receiver operating characteristic curves (AUCs) of the label propagation model and label spreading model are 0.979 and 0.999, respectively; and the AUCs of the Gaussian mixture model and logistic regression model are 0.800 and 0.817, respectively. Therefore, the data modeling efficiency of the label propagation model and label spreading model is comparable to that of the Gaussian mixture model and logistic regression model in detecting Fe-mineralization anomalies. However, the performance of the label propagation model and label spreading model are significantly better than that of the Gaussian mixture model and logistic regression model. It can be concluded that the two graph inference algorithms are potentially useful high-performance tools for detecting mineralization anomalies from geochemical exploration data.

Constructing a high-performance self-training model based on support vector classifiers to detect gold mineralization-related geochemical anomalies for gold exploration targeting

In an area where mineral deposits have been discovered, both anomaly detection algorithms and supervised classification algorithms can be adopted to detect mineralization-related geochemical anomalies for mineral exploration targeting. However, anomaly detection algorithms themselves cannot use known mineral deposits as the supervisors to improve the performance of geochemical anomaly detection models. Supervised classification algorithms can not properly deal with the extreme class-imbalance of geochemical exploration data in the establishment of classification models. Therefore, neither anomaly detection models nor supervised classification models perform well in the detection of mineralization-related geochemical anomalies. In order to obtain a high-performance mineral exploration targeting model, the self-training algorithm was adopted to construct a self-training model based on support vector classifiers to detect gold mineralization-related geochemical anomalies in the Chengde area in Hebei Province (China). The self-training model was compared with the support vector classification model, logistic regression model and five dictionary learning models in terms of area under the curve (AUC) and lift index. The AUC value of the self-training model (0.89) is much higher than those of the two supervised classification models (0.81–0.82) and five anomaly detection models (0.79–0.81). The lift index of the self-training model (11.87) is also much higher than those of the two supervised classification models (2.77–4.04) and five anomaly detection models (2.64–3.48). Therefore, the self-training model performs much better than the two supervised classification models and five anomaly detection models in the detection of gold mineralization-related geochemical anomalies. The gold mineralization-related geochemical anomalies detected by the self-training model highly conform to the metallogenic and geological features in the study area. Therefore, it is a prospective method to establish a self-training model to detect mineralization-related geochemical anomalies in an area where mineral deposits have been discovered.

Combining neighbourhood component analysis with dictionary learning algorithms to improve the performance of dictionary learning models for geochemical anomaly detection

In geochemical exploration, a geochemical anomaly detection model is usually established to describe the population distribution of geochemical data, and samples that do not conform to the model are identified as geochemical anomalies. Because the establishment of a geochemical anomaly detection model does not make use of the relationship between geochemical elements and mineralization, its performance for mineral exploration targeting is affected to a certain extent. For this reason, neighbourhood component analysis and dictionary learning algorithms were combined to detect geochemical anomalies associated with gold mineralization in the Chengde area in Hebei Province, China. Neighbourhood component analysis was used to transform geochemical data from the input space into the neighbourhood component space to enhance the separability between the geochemical anomalies associated with gold mineralization and the background. Dictionary learning models for geochemical anomaly detection were established in the neighbourhood component space. The performance of the dictionary learning models established in the neighbourhood component space was compared with that of corresponding models established in the input space in geochemical anomaly detection. The results show that the former models are superior to the latter. In addition, there is a strong consistency between the mineral exploration targeting results and metallogenic characteristics of the study area. Therefore, combining neighbourhood component analysis and dictionary learning algorithms can improve the performance of dictionary learning models in geochemical anomaly detection.

谷子部分区试品种在承德地区种植的农艺性状调查分析

谷子部分区试品种在承德地区种植的农艺性状调查分析

Remote Sensing Assessment of Safety Risk of Iron Tailings Pond Based on Runoff Coefficient.

Iron tailings ponds are engineered dam and dyke systems used to capture iron tailings. They are high-risk hazards with high potential energy. If the tailings dam broke, it would pose a serious threat to the surrounding ecological environment, residents’ lives, and property. Rainfall is one of the most important influencing factors causing the tailings dam break. This paper took Chengde Area, a typical iron-producing area, as the study area, and proposed a remote sensing method to evaluate the safety risk of tailings ponds under rainfall condition by using runoff coefficient and catchment area. Firstly, the vegetation coverage in the study area was estimated using the pixel dichotomy model, and the vegetation type was classified by the support vector machine (SVM) method from Landsat 8 OLI image. Based on DEM, the slope of the study area was extracted, and the catchment area of the tailings pond was plotted. Then, taking slope, vegetation coverage, and vegetation type as three influencing factors, the runoff coefficient was constructed by weight assignment of each factor using analytic hierarchy process (AHP) model in both quantitative and qualitative way. Finally, the safety risk of tailings ponds was assessed according to average runoff coefficient and catchment area in the study area. The results showed that there were 124 low-risk tailings ponds, 16 moderate-risk tailings ponds, and 4 high-risk tailings ponds in the study area. This method could be useful for selecting targeted tailings ponds for focused safety monitoring. Necessary monitoring measurements should be carried out for the high-risk and moderate-risk tailings ponds in rainy season.

Further paleomagnetic results from the ~ 155 Ma Tiaojishan Formation, Yanshan Belt, North China, and their implications for the tectonic evolution of the Mongol–Okhotsk suture

A new paleomagnetic study on well-dated (~155Ma) volcanic rocks of the Tiaojishan Formation (Fm) in the northern margin of the North China Block (NCB) has been carried out. A total of 194 samples were collected from 26 sites in the Yanshan Belt areas of Luanping, Beipiao, and Shouwangfen. All samples were subjected to stepwise thermal demagnetization. After removal of a recent geomagnetic field viscous component, a stable high temperature component (HTC) was isolated. The inclinations of our new data are significantly steeper than those previously published from the Tiaojishan Fm in the Chengde area (Pei et al., 2011, Tectonophysics, 510, 370–380). Our analyses demonstrate that the paleomagnetic directions obtained from each sampled area were strongly biased by paleosecular variation (PSV), but the PSV can be averaged out by combining all the virtual geomagnetic poles (VGPs) from the Tiaojishan Fm in the region. The mean pole at 69.6°N/203.0°E (A95=5.6°) passes a reversal test and regional tilting test at 95% confidence and is thus considered as a primary paleomagnetic record. This newly determined pole of the Tiaojishan Fm is consistent with available Late Jurassic poles from red-beds in the southern part of the NCB, but they are incompatible with coeval poles of Siberia and the reference pole of Eurasia, indicating that convergence between Siberia and the NCB had not yet ended by ~155Ma. Our calculation shows a ~1600-km latitudinal plate movement and crustal shortening between the Siberia and NCB after ~155Ma. In addition, no significant vertical axis rotation was found either between our sampled areas or between the Yanshan Belt and the major part of the NCB after ~155Ma.

Recovery of phosphorite from coarse particle magnetic ore by flotation

Chengde area in China is rich in magnetite resources containing phosphorus. It is meaningful to comprehensively recover phosphorus from the tailings of magnetic separation by flotation. However, the phosphorite particles are severely coarse with only 25% particles in size of −0.074mm approximately and over 50% particles in size of +0.25mm. The coarse particle settlement in conventional flotation cell usually appeared when the minerals are processed by flotation. In order to solve particle settling problem, JJF flotation cell is specially optimized to strengthen suspension capacity and circulation capacity for coarse particles, and the industrial experiment was carried out to recover phosphorite from the tailings of magnetite separation with 160m3 optimized JJF flotation cell. The industrial experiment results prove good adaptability of the optimized 160m3 flotation cell for coarse particles. When the feed grade of P2O5 is 2.12%, the concentrate grade is up to 30.68%, tailing grade is 1.14%, recovery is 47.97% and the yield of concentrate is 3.35%. Good metallurgical performances are achieved. The comprehensive utilization of phosphorus resources could be significantly improved in this area in the future.

The Research of Shaoguo Tunnel Failure Characteristics of Triaxial and Acoustic Emission Characteristics

This is accomplished by a variety of indoor rock mechanics experiments and experimental sound emission, taking Chengde area of Hebei Province the tunnel rock made of rock as research object to simulate various loading rock failure process. To analysis the data obtained of acoustic emission and Dynamics in the process obtaining the characteristic parameters of rock failure process in order to Monitor and Forecast. Through the three axial compression test and the process of acoustic emission signal acquisition discussed the relation between the mechanical parameters of rock acoustic emission parameters of stress. The characteristic parameters of acoustic emission signals with stress and time change through analyzing the acoustic Emission characteristics of each stage in the process of rock failure provide the basis for the prediction of rock burst.

Spatio-temporal distribution and tectonic settings of the major iron deposits in China: An overview

China has a rich reserve of iron ores and hosts most of the major types of iron deposits recognized worldwide. However, among these, the banded iron formation (BIF), skarn, apatite–magnetite, volcanic-hosted, sedimentary hematite and magmatic Ti–Fe–(V) deposits constitute the most economically important types. High-grade iron ores (>50% Fe) are relatively rare, and are mostly represented by the skarn-type. Most of the BIF deposits formed in the Neoarchean, with a peak at ~2.5Ga, and are mainly distributed in the North China Craton. The majority of these is associated with volcanic rocks, and therefore belongs to the Algoma-type. The superior-type BIF deposits formed during the Paleoproterozoic occur subordinately (ca. 25%), and are related mainly to rifts (or passive continental margins). In addition, minor Superior-type BIF deposits have also been recognized. The skarn iron deposits are widely distributed in China, especially in the uplifted areas of eastern China, and form several large iron ore clusters. These ore deposits are genetically associated with intermediate, intermediate-felsic and felsic intrusions with a peak age of formation at ca. 130Ma. They display common characteristics including alteration and nature of mineralization. The apatite–magnetite deposits occurring in the Ningwu and Luzong Cretaceous terrigenous volcanic basins along the Middle–Lower Yangtze River Valley, are spatially and temporally associated with dioritic subvolcanic intrusions. The ores in this type are characterized by magnetite and apatite. The volcanic-hosted iron deposits are associated with submarine volcanic-sedimentary sequences, and are widely distributed in the orogenic belts of western China, including Western Tianshan, Eastern Tianshan, Beishan, Altay, Kaladawan area in the eastern part of the Altyn Tagh Mountain and southwestern margin of South China Block. These deposits show a considerable age range, from Proterozoic to Mesozoic, but with more than 70% were formed in the Paleozoic, especially during the Late Paleozoic. The metallogenesis in these deposits can be correlated to the space–time evolution of the submarine volcanism, and their relationship to volcanic lithofacies variation, such as central, proximal and distal environments of ore formation. The sedimentary hematite deposits are widespread in China, among which the “Xuanlong-type” in the North China Craton and the “Ningxiang-type” in the South China Block are the most economically important. All these deposits formed during transgressions in a shallow-marine environment. Magmatic Ti–Fe–(V) deposits are dominantly distributed in the Panxi area in Sichuan province and Chengde area in Hebei province. They are dominated low-grade disseminated ores, and unlike the other types of iron deposits, associated sulfide deposits are absent, with magnetite, titanomagnetite and ilmenite as the dominant ore minerals. In the Panxi area in the central Emeishan large igneous province along the western margin of South China Block, the ores are hosted in the ca. 260Ma mafic layered intrusions, whereas the ores in the Chengde area are associated with the Mesoproterozoic anorthosite complex. The distinct spatio-temporal characteristics of the various iron deposits in China correlate with the multiple tectono-magmatic events associated with the prolonged geological history of the region involving accretion, assembly and rifting.

The Vanadium Titanium Magnetite Tailings of Chengde Area Comprehensive Recycling Situation and Development Trend

Summarized the siutuation of the vanadium titanium magnetite tailings recycling in Chengde, TiO2 in ilmenite tailings grade almost more than 2%, caused by TiO2 resources waste, put forward by gravity separation, magnetic separation,and flotation combined process method for titanium iron ore tailings reelection, and put forward the technological process selection by reducing the grinding fineness, increasing number of handpick and scavenging, adopt combination process and increasing flotation process to increase the recovery rate of TiO2, reduce TiO2 grade in the tailings.

Analysis of the risk factors for influenza A (H1N1) pneumonia

Pneumonia is the most common complication of influenza A (H1N1). However, there has been no identification of any single initial symptom as an independent risk factor. In 2009, 206 patients were diagnosed with H1N1 in the Chengde area, China and they were assembled in the Chengde Hospital for Infectious Diseases. The diagnosis and treatment were performed in accordance with the Protocol for Diagnosis and Treatment of Influenza A (H1N1) (2009, third edition), issued by the General Office of the Ministry of Health, with detailed records by the medical staff assigned by the Bureau of Health of the Chengde government. All the patients had viral nucleic acid-positive throat swabs detected with quantitative reverse transcription polymerase chain reaction (qPCR). Based on the final imaging findings, patients were divided into the pneumonia and non-pneumonia groups for this case control study. The univariate analysis demonstrated that the ratios of patients aged 0-5 and ≥45 years, with underlying diseases, with initial symptoms including cough, expectoration and dyspnea and with onset-to-treatment interval of >48 h were higher in the pneumonia compared to the non-pneumonia group (P<0.001, P<0.001, P=0.018, P<0.001, P<0.001 and P<0.001, respectively). The multivariate logistic regression analysis demonstrated that age 0-5 years, presence of underlying diseases, expectoration as the initial symptom and onset-to-treatment interval of >48 h were the independent risk factors for pneumonia with relative odds ratios (OR) and 95% confidence intervals (95% CIs) of 6.120 and 1.779-21.088, 11.188 and 2.021-61.935, 5.263 and 2.042-13.562, and 22.873 and 6.110-85.631, respectively (P<0.01). Therefore, it is recommended that patients with H1N1 presenting with expectoration as the initial symptom be treated with caution during influenza pandemics.

A paleomagnetic study from the Late Jurassic volcanics (155 Ma), North China: Implications for the width of Mongol–Okhotsk Ocean

A new paleomagnetic study has been carried out on the Tiaojishan Formation effusives in the Chengde area, North China Block (NCB). The age of the Tiaojishan Formation volcanics was recently dated from 153 Ma to 161 Ma by U–Pb and 40Ar– 39Ar. A total of 154 samples from 16 sites within the Tiaojishan Formation were collected. Detailed stepwise thermal demagnetization allows us to isolate two components from 77 oriented drill-core samples. A low-temperature component identified in most samples falls close to the present local Earth field direction. Characteristic remanent directions obtained from high-temperature pass-reversal test yield a pole position at 240.3°E, 59.9°N with α95 = 6.8°. Combined with coeval paleopoles obtained from the Siberia Block (SIB), these results show a significant paleolatitude displacement between the NCB and SIB, which indicates that the Mongol–Okhotsk Ocean was still open with ~ 3000 km width in its eastern part at ca. 155 Ma.

Constraints on the late Mesozoic regional angular unconformity in West Liaoning–North Hebei by LA-ICP-MS dating

The angular unconformity between the Tuchengzi Formation and the Zhangjiakou Formation in the Chengde area is commonly regarded as the direct evidence of boundary between Jurassic and Cretaceous systems, and as the evidence of tectonic transformation interfaces in Mesozoic Era in the West Liaoning-North Hebei. Moreover, the lower boundary age of the Zhangjiakou Formation in the Chengde was to be thought about 135 Myr in the past. Based on the detailed study and systematic sampling in Yixian-Beipiao, Lingyuan, Chengde and Luanping areas in the field, the authors obtained the following isotopic ages by means of LA-ICP-MS dating: In the Lingyuan area, the Zhangjiakou Formation and the Yixian Formation are in angular unconformable contact; the age range of the Zhangjiakou Formation volcanic rock is from 133 to 130 Myr (seven samples), and that of the Yixian Formation volcanic rock is from 126 to 121 Myr (eleven samples). In the Luotuoshan of Chengde, the Tuchengzi Formation and the Zhangjiakou Formation are in angular unconformable contact; the age at the lower Zhangjiakou Formation is about 124 Myr (one sample), which reflects that the age range of the lower Zhangjiakou Formation is from 129 to 124 Myr combined with the previous study. In the Luanping basin, there is the largest sediment gap in upper Mesozoic, whose lower boundary age is confined in 131 to 130 Myr. In the Yixian-Beipiao area, the second Member of the Yixian Formation, making up more than 3/4 thickness of the Yixian Formation, was formed in 126 to 119 Myr, and the age of the Yixian Formation at the bottom is about 132 Myr. Moreover, the upper boundary age of the Tuchengzi Formation is about 136 Myr in the West Liaoning-North Hebei region. Based on the above isotopic data combined with the geological characteristics in the field in the West Liaoning--North Hebei region, it reflects that there was a widespread tectonic event in the period of 136 to 126 Myr (mainly in 130–126 Myr) in the West Liaoning-North Hebei, and it formed the late Mesozoic regional angular unconformity. Moreover, the lower boundary ages of the late Mesozoic regional angular unconformity in different areas are different, and the tectonics and the creature assemblage are different in the strata at the top and bottom of the regional angular unconformity. It implies that the late Mesozoic regional angular unconformity is a regional interface of tectonic transformation in Mesozoic Era in the West Liaoning-North Hebei.