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The transition-metal-catalyzed cyclopropanation of alkenes by the decomposition of diazo compounds is a powerful and straightforward strategy to produce cyclopropanes, but is tempered by the potentially explosive nature of diazo substrates. Herein we report the Mo-catalyzed regiospecific deoxygenative cyclopropanation of readily available and bench-stable 1,2-dicarbonyl compounds, in which one of the two carbonyl groups acts as a carbene equivalent upon deoxygenation and engages in the subsequent cyclopropanation process. The use of a commercially available Mo catalyst afforded an array of valuable cyclopropanes with exclusive regioselectivity in up to 90 % yield. The synthetic utility of this method was further demonstrated by gram-scale syntheses, late-stage functionalization, and the cyclopropanation of a simple monocarbonyl compound. Preliminary mechanistic studies suggest that phosphine (or silane) acts as both a mild reductant and a good oxygen acceptor that efficiently regenerates the catalytically active Mo catalyst through reduction of the Mo-oxo complexes.

A two-dimensional (2D) coordination polymer [Co2(OH)(H2O)2(cbca)·2H2O]n (CP 1) (H3cbca: 4′-(1-carboxyethoxy)-[1,1′-biphenyl]-3,5-dicarboxylic acid) with a topological (3,6) connection point structure was synthesized by a hydrothermal method. Single-crystal X-ray analysis revealed that the 2D structure is comprised of tetranuclear cobalt clusters and semi-rigid cbca ligands connected through strong Co–O bonds. It has been found that CP 1 can be used as an effective multiple fluorescent sensor for detecting iron ions, specific nitro compounds, antibiotics and pesticides. Further investigations show that the fluorescence quenching originates from different mechanisms. Among them, the redshift phenomenon generated during the detection of some analytes can also effectively distinguish analytes, which is rarely mentioned in previous studies.

In order to study the pollution status of heavy metals in coastal agricultural soils, this paper took a vegetable base of the coastal area in Southeast China as the research object to analyze the distribution status of seven heavy metals, namely, Cr, Ni, Cu, Zn, As, Cd and Pb, in the soil depth of 0–20 cm and 20–40 cm, based on Kriging interpolation method. In terms of soil spatial variation eigenvalues, The element As between the two layers showed the strongest spatial correlation, followed by Pb and Ni. Cr, Cu, Zn and Cd showed moderate spatial correlation. It was concluded from the interpolation results that Cr and Ni elements, especially in the surface soil, had a similar spatial distribution characteristics.

Two new Zn centered coordination polymers (CPs), namely, [Zn(BDA)] (1) and [Zn3(BTA)2(H2O)4] (2) have been synthesized from 4′-hydroxy-[1,1′-biphenyl]-3,5-dicarboxylic acid (H2BDA) and 4-hydroxy-[1,1′-biphenyl]-3,3′,5,5′-tetracarboxylic acid (H3BTA) under solvothermal conditions. Their structures were determined by single crystal X-ray diffraction and further characterized by elemental analysis, IR, TGA and X-ray powder diffraction. CP 1 based on H2BDA is a 3D framework featuring (4,4)-connected net topology with a point symbol of {43.62.8}, while 2 with H3BTA as ligand displays a (2,3)-connected net with a point symbol of {63} and hcb topological type structure. Both CPs 1 and 2 exhibited luminescence identification properties toward nitro aromatic compounds and Fe3+ cation.

Soil acidification caused by long‐term nitrogen (N) fertilizer applications has been a growing concern for dryland crop production in both tilled and no‐till soils in the Pacific Northwest (PNW). Many no‐till soils have stratified soil pH in the 5–10 cm depth due to repeated N fertilizer applications at this depth. In the PNW, the practice of liming to correct low soil pH is complicated due to lack of affordable lime sources and because the inherent difficulty in ameliorating stratified soil acidity in no‐till systems. An intact soil‐column incubation study was conducted to investigate whether mixing lime materials with lignin‐containing black liquor—a by‐product from the pulp industry—could elevate soil pH change in both conventional and no‐till systems and expedite vertical downward movement of lime in no‐till system. Results indicate that mixing lime with black liquor has the potential to not only elevate the increase in soil pH in both conventional till and no‐till systems, but also accelerate downward movement of lime to correct soil pH below the soil surface. Mixing agricultural lime or super fine micro lime with black liquor increased soil pH to a depth of 25–30 cm within 147 days after surface application to a no‐till soil.

Elevation is important for determining the nutrient biogeochemical cycle in forest ecosystems. Changes in the ecological stoichiometry of nutrients along an elevation gradient can be used to predict how an element cycle responds in the midst of global climate change. We investigated changes in concentrations of and relationships between nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) in the leaves and roots of the dominant tree species, Castanopsis fargesii, along an elevation gradient (from 500 to 1,000 m above mean sea level) in a subtropical natural forest in China. We analyzed correlations between C. fargesii’s above-ground biomass and stoichiometry with environmental factors. We also analyzed the soil and plant stoichiometry of this C. fargesii population. Our results showed that leaf N decreased while leaf K and Ca increased at higher elevations. Meanwhile, leaf P showed no relationship with elevation. The leaf N:P indicated that C. fargesii was limited by N. Elevation gradients contributed 46.40% of the total variance of ecological stoichiometry when assessing environmental factors. Our research may provide a theoretical basis for the biogeochemical cycle along with better forest management and fertilization for this C. fargesii population.

Accurate reading of a pointer meter is a crucial task in complex environments, such as substations, the military and aerospace. Current recognition algorithms are mainly used to identify the same type and a non-tilt meter, which has limited application in a real environment. In this paper, we propose a novel end-to-end intelligent reading method for a pointer meter based on deep learning, which locates the meter and extracts the pointer simultaneously without any prior information. In particular, the pointer is directly and precisely extracted using the designed semi-pointer detection method without any handcrafted features designed in advance, which avoids accumulated error caused by preprocessing. Based on the extracted panel object, including the semi-pointer, panel center and scale characters, the indicated value of the pointer is obtained by a local angle method, which achieves better performance than the traditional angle method by referring to the neighboring scale lines of the pointer. Experimental results demonstrate that the method is faster and more effective than some common methods. It is worth noting that this study has the advantage of being able to recognize pointer meters under complex conditions such as tilt, rotation, blur and illumination. This is acceptable for the actual application requirements in a real environment with a recognition accuracy of 99.20% and an average reference error of 0.34%.

Two Europium(III) complexes [Eu(NCP)(1,4-bdc)]n·0.5H2O (1) and [Eu(NCP)(4,4′-bpdc)]n·0.75H2O (2) (HNCP = 2-(4-carboxyphenyl)imidazo(4,5-f)-(1,10)phenanthroline, 1,4-H2bdc = benzene-1,4-dicarboxylic acid, 4,4′-H2bpdc = 4,4′-biphenyldicarboxylic acid) have been hydrothermally synthesized and characterized via elemental analysis, infrared spectrometry and single crystal X-ray diffraction. Structural analyses revealed that two complexes all exhibit three-dimensional network structures with a Schlafli symbol of 412·63 topologies. In addition, these two metal-organic frameworks present excellent thermal stabilities and the preferential adsorption of CO2 over N2.

Residential solar photovoltaic (PV) installations have boomed in China over recent years. However, knowledge about the economic performance of residential PV investments is still limited. Therefore, this study attempts to make a complete economic assessment of residential PV systems at the county-level. After a brief description of China’s incentive policies, a model is constructed with economic measures of internal rate of return and payback period based on discounted cash flow. Then, a reference PV system of 5 kWp is investigated in 2181 geographical areas using scenario analysis and sensitivity analysis. There is breakeven for residential PV investments all over China even without incentives. The areas with good performance of residential PV investments are located mostly in Northern China (Shandong and Hebei), Southern China (Guangdong and Hainan), northeast China (Liaoning, Jilin, and Heilongjiang), and southwest China (Sichuan and Tibet). The boom of residential PV installations is likely to recede in 2021 as the population with acceptable profitability declines since the central and local subsidies expire. But some excellent residential PV suppliers have the potential to compensate for the performance decrease in many ways such as increasing annual generation and reducing initial cost. This paper contributes by providing some insights into the economic performance and market of residential PV installations in China.

A fast normalized cross correlation (FNCC) based machine vision algorithm was proposed in this study to develop a method for detecting and counting immature green citrus fruit using outdoor colour images toward the development of an early yield mapping system. As a template matching method, FNCC was used to detect potential fruit areas in the image, which was the very basis for subsequent false positive removal. Multiple features, including colour, shape and texture features, were combined in this algorithm to remove false positives. Circular Hough transform (CHT) was used to detect circles from images after background removal based on colour components. After building disks centred in centroids resulted from both FNCC and CHT, the detection results were merged based on the size and Euclidian distance of the intersection areas of the disks from these two methods. Finally, the number of fruit was determined after false positive removal using texture features. For a validation dataset of 59 images, 84.4 % of the fruits were successfully detected, which indicated the potential of the proposed method toward the development of an early yield mapping system.

The Fukeshan Cu (Mo) deposit is a newfound porphyry deposit in the northern Great Xing&rsquo, an Range (GXR), northeast China. In this paper, we present results of chalcopyrite Re&ndash, Os geochronology, microthermometry of the fluid inclusions (FIs), and isotopic (H&ndash, O&ndash, S&ndash, Pb) compositions of the Fukeshan Cu (Mo) deposit. Its ore-forming process can be divided into sulfide-barren quartz veins (A vein, stage I), quartz + chalcopyrite + pyrite veins (B vein, stage II), quartz + polymetallic sulfide veins (D vein, stage III), and barren quartz + carbonate &plusmn, pyrite veins (E vein, stage IV), with Cu mineralization mainly occurred in stage II. Three types of FIs are identified in this deposit: liquid-rich two-phase (L-type) FIs, vapor-rich two-phase (V-type) FIs, daughter mineral-bearing three-phase (S-type) FIs. The homogenization temperatures of primary FIs hosted in quartz of stages I&ndash, IV are 381&ndash, 494 &deg, C, 282&ndash, 398 &deg, C, 233&ndash, 340 &deg, C, and 144&ndash, 239 &deg, C, with salinities of 7.2&ndash, 58.6, 4.8&ndash, 9.9, 1.4&ndash, 7.9, and 0.9&ndash, 3.9 wt. % NaCl equivalent, respectively. FIs microthermometry and H&ndash, O isotope data suggest that the ore-forming fluids were magmatic in origin and were gradually mixed with meteoric water from stages II to IV. Sulfur and lead isotope results indicate that the ore-forming materials of the Fukeshan Cu (Mo) deposit were likely to have originated from Late Jurassic intrusive rocks. The available data suggest that fluid cooling and incursions of meteoric water into the magmatic fluids were two important factors for Cu precipitation in the Fukeshan Cu (Mo) deposit. Chalcopyrite Re&ndash, Os dating yielded an isochron age of 144.7 &plusmn, 5.4 Ma, which is similar to the zircon U&ndash, Pb age of the quartz diorite porphyry, indicating that Late Jurassic quartz diorite porphyry and Cu mineralization occurred contemporaneously.

More than four hundred million people suffer from respiratory diseases each year. Respiratory diseases are associated with a large disease burden. Heatwaves and cold spells, the two most common extreme weather events, have been shown to have crucial negative effects on the prevalence of respiratory diseases. However, impacts of extreme weather on the prevalence of respiratory diseases has been largely overlooked in western China, where more intense and frequent extreme temperature events have been occurring over the past decades. This research gap will obtain an attribution bias in the effects of extreme weather events on the prevalence of respiratory diseases. Therefore, in this study, we analyzed the impact of heatwaves and cold spells on the morbidity of respiratory diseases using a distributed lag nonlinear model with daily disease cases from 2013 to 2016 in Lanzhou, one of the largest cities in western China. A reverse U-shaped relationship depicted the relationship between temperature and the morbidity of respiratory diseases. The highest relative risk was found at 2.6 °C by 1.15 (95% confidence interval: 1.09–1.21). Furthermore, we found a significant decrease in the relative risk for heatwaves and a significant increase in the relative risk of cold spells when the temperature exceeded the corresponding threshold by 1 °C. Heatwaves and cold spells play harvest effects on the morbidity of respiratory diseases. Our study suggest that the relative risk of respiratory diseases will increase as the climate warms in the future, and thus a preventive system is needed for individuals and medical policy-makers.

Natural disasters play significant destructive roles in agricultural production. Agriculture has substantially altered the biogeochemical silica cycle via the harvest of the grain and straw of silica-rich crops. China is a developing agricultural country that experiences frequent natural disasters. Although the spatiotemporal changes in the occurrence of natural disasters are well known for individual disaster events, the study of the effects of multiple disasters on cereal crop productivity and the resultant silica harvest is still relatively new. To make the connection between natural disasters and the silica biogeochemical cycle, we compiled a dataset of natural disasters and crop production and silicon contents in cereal crops (rice, wheat, and maize) in China using province-level data from 1988 to 2016. Our results show that the area affected by natural disasters declined significantly after 2000, and changes in the area affected by natural disasters varied at the province level. From 1988 to 2016, the total silica losses from grain and straw harvests due to natural disasters were 7.14 and 53.10 million tons, respectively. Half of the silica loss in more than half of the provinces was caused by drought. Our study suggests that drought prevention will increase the size of the silica sink and thereby increase the size of the carbon sink in China's agriculture.

We use stochastic modeling, the permeability tensor principle, and the finite-element method to construct a discrete complex model of a fracture network, an equivalent continuum model, and a continuum model for anisotropic seepage in a complex fracture network described using the permeability tensor. Solution of these models lets us describe the process of gas production from reservoirs after hydraulic fracturing. In these models, we fully take into account the geological structural features of hydraulically fractured tight reservoirs and the morphology of three-dimensional fracture networks. We obtain a new model for predicting production from tight gas reservoirs after hydraulic fracturing.

An integrated technology combining water plugging and acidizing in the same flooded oil well is studied with the goal of controlling the water content of the oil and attaining a comprehensive synergy. For this, a micro- and macro-fracture porosity model and four models with other kinds of fractures are built. The feasibility of application of gel water plugging system in this integrated technology is evaluated. The experimental results show that the gel system is effective in high-permeability interlayer plugging (plugging degree >99%), but not very effective in low-permeability interlayers. Multi-slug injection reduces the impact of subsequent acidizing on the effectiveness of the pugging material. At the same time, the degree of oil recovery by displacement with water for the fracture porosity model is not high, but after polymer plugging system injection, recovery increases, reaching more than 60%. The proposed integrated technology was applied in two wells in the Chongqing oilfield. Oil production of each well increased on an average by 1 ton/day for more than one year.

Carbonate reservoirs are generally highly heterogeneous and fractured. Acid leak-off (infiltration) in acid hydrofracking is particularly serious because it reduces the acid efficiency and limits the fracture length from acid etching. As a result, a favorable reservoir away from the wellbore may fail to be connected, leading to poor results after acid hydrofracking. In this work, a new three-phase (oil—gas—acid) model of acid leak-off (infiltration) in acid hydrofracking of a reservoir that considers dissolution of rock by acid, changes in the width of natural fractures, and the effect of CO 2 was developed and a numerical calculation method was also given. The model allows precise description of the characteristics of acid leak-off in acid-fractured carbonate reservoirs. The results show that the mechanism of acid infiltration into fractured and porous (permeable) reservoirs differs widely. The acid penetrates deep into the reservoir mainly via wide natural fractures and only thereafter through microfissures and the matrix. The acid reacts most vigorously with the rock in wide fractures, leading to further widening of the natural fractures and increased infiltration of the acid into the fractures, which causes deeper penetration of the acid into the fractures. CO 2 reduces the acid leak-off rate, which is less at higher CO 2 concentrations.

The neural network ensemble (NNE) is a very effective way to obtain a good prediction performance by combining the outputs of several independently trained neural networks. Swarm intelligence is applied here to model the population of interacting agents or swarms that are able to self-organize. In this paper, we combine NNE and multi-population swarm intelligence to construct our improved neural network ensemble (INNE). First, each component forward neural network (FNN) is optimized by chaotic particle swarm optimization (CPSO) and gradient gescending (GD) algorithm. Second, in contrast to most existing NNE training algorithm, we adopt multiple obviously different populations to construct swarm intelligence. As an example, one population is trained by particle swarm optimization (PSO) and the others are trained by differential evolution (DE) or artificial bee colony algorithm (ABC). The ensemble weights are trained by multi-population co-evolution PSO-ABC-DE chaotic searching algorithm (M-PSO-ABC-DE-CS). Our experiments demonstrate that the proposed novel INNE algorithm is superior to existing popular NNE in function prediction.

Based on the characteristics of coalbed methane formations and the parallel-connection pipeline principle, we have formulated a model for calculating the pressure drop and the production of a horizontal multi-branch coalbed methane well. We have analyzed the effect of the number of side branches, the length and angle of intersection of the side branches with the main branch on the well productivity. The results show that with an increase in branch spacing, the productivity first decreases and then increases; with an increase in the angle of intersection and the length of the side branches, the productivity increases.

This study was conducted to identify blueberry fruit of different growth stages using natural outdoor images toward the development of a blueberry yield mapping system. As blueberries usually contain different maturity stages in a same branch, identification of blueberry fruit and their maturity stages from different background is very important for yield mapping. In this study, maturity stages of the fruit were divided into four categories: mature (m), near-mature (nm), near-young (ny) and young (y). A stepwised algorithm, termed 'color component analysis based detection (CCAD)' method, was developed and validated to identify blueberry fruit using outdoor color images. Firstly, a dataset was built using manually cropped pixels from training images. Three color components, red (R), blue (B) and hue (H), were selected using the forward feature selection algorithm (FFSA), and used to separate all fruit of four maturity stages from background through different classifiers. In this work, not only the traditional classifiers such as K-nearest neighbor (KNN), and naive Bayesian classification (NBC) were used, but another newly introduced 'supervised K-means clustering classifier (SK-means)' was also developed and applied to the dataset. In the second step, classifiers were built to separate a group of 'mature & near-mature' fruit from a group of 'near-young & young' fruit from all fruit pixels. Finally, classifiers were developed to separate mature fruit from near-mature fruit, and near-young fruit from young fruit. The classifiers obtained from these different steps were then applied to validation images, resulting in final identification. Cross validation was conducted using these different classifiers and their results were compared. KNN classifier yielded the highest classification accuracy (85-98%) from the validation set of the prebuilt pixel dataset collected from the training images in all separations. An one-way ANOVA was used to compare the performance of the three classifies, which shows KNN performed significantly better than other methods. The newly proposed 'SK-means' classifier yielded a fairly high accuracy (90%) for the separation of mature and near-mature fruit. The newly developed 'CCAD' method for blueberry was proved to be efficient for identifying blueberry fruit of different growth stages using natural outdoor color images toward the development of a blueberry yield mapping system.

There are a great amount of electronic meters equipped in the distribution substations, which were traditionally monitored by operators. On-site monitoring for risk assessment of these meters is very important. In this paper, we presented an advanced machine vision based automatic meter detection method toward the development of an online automatic meter reading intelligent inspection robot in substation. Firstly, the image received from the inspection robot was enhanced using histogram equalization. Then, the image was segmented into two parts based on the threshold obtained by Otsu’s method. Using these two parts, and the whole enhanced image, circular Hough transformation was applied on these three images and detected the circle with highest probability on them. The normalized correlation coefficients were calculated between the corresponding areas of those three circles from three images and the template image of SF6meter. Finally, the circle with highest correlation coefficient, which was higher than a certain threshold, was determined to be the meter. If it is lower than the threshold, the algorithm would decide that no meter was found in the image. The method was tested with 222 images obtained in one substation in Xi’an, Shanxi, China, and an 87.4% accuracy was achieved using these images, which indicated the potential of this method.

Farmers’ interest in precision nitrogen (N) fertilization has grown in the inland Pacific Northwest due to significant within-field spatial variability in yield and protein content. Mapping the spatial variability of yield, protein content, and total grain N uptake (Ng) can be a useful tool for post-harvest evaluation of N sufficiency that, in turn, can guide precision N fertilization. We evaluated the applicability of combining RapidEye satellite imagery-derived vegetation indices with topographic variables derived from high-resolution data obtained from proximal sensors to estimate winter wheat yield, protein content, and Ng. Results indicate that the normalized difference vegetation index (NDVI), the normalized difference red edge index (NDRE), terrain curvature, slope, aspect, topographic wetness index, and solar radiation are the most relevant co-variables that contribute to spatial variability of yield and Ng. Yield and Ng exhibited strong relationships with NDVI and NDRE from late June through early July during grain filling stage, with nearly all R2 > 0.6. We found greater yield and Ng values in concave-shaped terrain; 0-5° and 10-20° slopes; and flat, eastern, or northern aspects. In contrast, protein content exhibited weak relationships with vegetation indices and nearly all terrain factors. Prediction models demonstrated that these variables can provide good estimations of yield and Ng, with R2 of 0.848 and 0.864, respectively, and RMSE of 48.47 and 0.0136, respectively. Combining RapidEye-based NDRE with proximal sensor-based topographical factors shows great potential for accurately and efficiently estimating winter wheat yield and Ng, which can help guide N fertilizer management.

Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20 cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R2 for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected.

The spatial information of soil organic matter (SOM) is crucial for precision agriculture and environmental modeling. It is, however, difficult to obtain the regional details of SOM by dense sampling due to the high cost. Although a variety of interpolation methods are available for mapping SOM at regional scales, accurate prediction usually needs densely distributed samples and requires the interpolated variable to meet some constraints such as spatial stationarity. This paper introduces the Geographically Weighted Regression (GWR) technique as an alternative approach for SOM mapping. We interpolated the spatial distribution of SOM based on a limited number of samples with the incorporation of multiple independent variables. We also compared GWR with the ordinary least squares regression approach in mapping SOM. Results indicated that GWR could capture more local details and improve the prediction accuracy. However, more attention should be paid to the selection of independent variables.

Hyperspectral (HS) imaging is becoming more important for agricultural applications. Due to its high spectral resolution, it exhibits excellent performance in disease identification of different crops. In this study, a novel method termed ‘extended spectral angle mapping (ESAM)’ was proposed to detect citrus greening disease (Huanglongbing or HLB), which is a very destructive disease of citrus. Firstly, the Savitzky–Golay smoothing filter was used to remove spectral noise within the data. A mask for tree canopy was built using support vector machine, to separate the tree canopies from the background. Pure endmembers of the masked dataset for healthy and HLB infected tree canopies were extracted using vertex component analysis. By utilizing the derived pure endmembers, spectral angle mapping was applied to differentiate between healthy and citrus greening disease infected areas in the image. Finally, most false positive detections were filtered out using red-edge position. An experiment was carried out using an HS image acquired by an airborne HS imaging system, and a multispectral image acquired by the WorldView-2 satellite, from the Citrus Research and Education Center, Lake Alfred, FL, USA. Ground reflectance measurement and coordinates for diseased trees were recorded. The experimental results were compared with another supervised method, Mahalanobis distance, and an unsupervised method, K-means, both of which showed a 63.6 % accuracy. The proposed ESAM performed better with a detection accuracy of 86 % than those two methods. These results demonstrated that the detection accuracy using HS image could be enhanced by focusing on the pure endmember extraction and the use of red-edge position, suggesting that there is a great potential of citrus greening disease detection using an HS image.

A new Mn(II) coordination polymers based on the 1,10- phenanthroline derivative and flexible dicarboxylate, namely [Mn2(L)2(trans-1,4-chdc)] (1), where L = 11-fluoro-dipyrido[3,2-a: 2′,3′-c]phenazine and trans-1,4-chdc = 1,4-cyclohexanedicar- boxylate, has been successfully synthesized under hydrothermal condition. Compound 1 shows a one-dimensional chain structure. The neighboring chains are extended into a 3D supramolecular architecture by π-π interactions. Its thermal property was also investigated.

Soil organic matter (SOM) is an important component of soils, and knowing the spatial distribution and variation of SOM is the premise for sustainably utilizing soils. The objective of this study was to compare geographically weighted regression (GWR) with regression kriging (RK) for estimating the spatial distribution of SOM using field-sample data in SOM and auxiliary data in correlated environmental variables (e.g., elevation, slope, ferrous minerals index, and Normalized Difference Vegetation Index). Results showed that GWR was a relatively better method and could provide promising results for SOM prediction in comparison with RK. The map interpolated by GWR showed similar spatial patterns influenced by environmental variables and the nonapparent effect of data outliers, but with higher accuracies, compared to that interpolated by RK.

The robustness of K-means clustering is poor in non-spherical distribution data, in order to improve the universal ability of clustering algorithms, the cross-entropy distance measure was used to replace the Euclidean distance measure . Contour let transform, not only has characteristics of multi-resolution, locality and critical sampling which wavelet has, but also has the characteristics of multiple decomposition directions and anisotropy which wavelets lack. So we combine the modified K-means clustering and Contour let transform to apply for image fusion. Experimental results show that this method is feasible.

To ensure the fluids selected to drill and complete the well would simplify the operation for the oil and gas development in the petroleum industry, a natural vegetable gum drilling and completion fluids system is developed for the industrial intelligent application. As the system combines the advantage of the drilling fluid and completion fluid, it need not change the two different fluids during the operation, which is beneficial to the intelligent operation. In addition, the formulation of the proposed system has mainly taken the environment factor into consideration as the environmental protection has become main concern before the implementation of the oil and gas exploration. An extensive laboratory work of the natural vegetable gum drilling and completion fluids system is carried out, including the formulation study of the detailed system and the corresponding performance evaluation. In the system, the vegetable gum is chosen as raw material and TLJ-1 is optimally selected as the major treatment agent in the natural vegetable gum drilling and completion fluid system. The LV-CMC, polyglycol and QS-2 are taken as the auxiliary treatment agents for the system. And the three formulations, i. e. the solids-free fluid system, the low-solids fluid system and the weighting fluid system have been presented in this paper. The laboratory analysis has demonstrated that the prosperities of the system are proper for the industrial intellectual application, with the temperature resistance capability of 315 ℉.

Violation of discipline by students may have a direct impact on the school spirit and academic atmosphere construction of colleges and universities and is becoming a hot issue which is brought to public attention. Survey and analysis have been conducted on violation of discipline by students of Petroleum and Natural Gass Engineering College from such three aspects as type of discipline violation, type of punishment as well as education background of students being punished so as to better adapt to the student management work under new situation and timely and accurately grasp the trend of student discipline violation in current stage. The paper aims to analyze and explore the reasons of discipline violation by students of Petroleum and Natural Gass Engineering College and its inherent law through deep analysis on both subjective factors and objective factors leading to disciplinary offence. Corresponding precautionary measures and countermeasures have been put forward to reduce and even avoid the occurrence of disciplinary offence by students.

The construction of practice teaching base on campus which is close to the front of production line and which can reflect the real environments of oil fields is an important condition to train applied and high skilled talents of petroleum engineering major. This paper, by combining the requirements for practice teaching base for the construction of petroleum engineering major, summarizes the construction targets of practice teaching base for petroleum engineering, and further puts forward the idea and principle of constructing the practice teaching base, analyzes the functions which shall be met for practice teaching base for petroleum engineering and discusses the construction mode of practice teaching base.

The necessity on construction for practical teaching base on campus for petroleum engineering is described from such three aspects as requirement of practical talents training, requirement of vocational skills training as well as requirement of subject construction and campus culture construction in this article, the construction mode of practical teaching base on campus for petroleum engineering is discussed and the advantage of establishing the practical teaching base on campus cooperated jointly by campus and enterprise is analyze. Finally, the efficiency produced by the practical teaching on campus is summarized with combination of the operation condition of practical teaching base on campus for petroleum engineering.

To simplify the operation for the oil and gas development for drill and complete the well in the petroleum industry, a natural vegetable gum drilling and completion fluids system is developed for the industrial control and application. As the system combines the advantage of the drilling fluid and completion fluid, it need not change the two different fluids during the operation, which is beneficial to the operation control. In addition, it can improve the implementation of the oil and gas exploration for industrial application. An extensive laboratory work for the high-temperature resistance capability of the natural vegetable gum drilling and completion fluids system is carried out, including the formulation study of the detailed system and the corresponding performance evaluation. In the system, with the basic formula of the natural vegetable gum drilling and completion fluids system, several new formulas beneficial to improve the high-temperature resistance capability of the system have been described. The corresponding laboratory analysis has demonstrated that the prosperities of the system, with the temperature resistance capability up to 305℉, are proper for the industrial application.

Activated carbon fibers (ACFs) were surface modified with oxygen plasma at low pressure. The novel effects of the plasma treatment on the microstructural properties of the ACFs were characterized using the Brunauer, Emmett, and Teller method and scanning electron microscopy. Micropores developed on the ACFs. Moreover, the specific surface area and micropore volume increased by 10% at a certain plasma treatment time and power. The changes in the structural properties of the ACFs are discussed in detail with the respect of plasma etching. X-ray photoelectron spectroscopy revealed new oxygen-containing groups, such as CO, CO, and OCO, had formed on the surface of the ACFs after plasma treatment. Plasma surface oxidative reactions such as the generation of radicals, the combination of the radicals and active oxygen species in the plasma chamber, and the generation of the various oxygen-containing groups are believed to have occurred. The effect of the plasma treatment parameters such as plasma treatment time ...

Three ceramic/epoxy composites were under investigation. The ceramic fillers include self-synthesized BaTiO 3 and commercial BaTiO 3 and Pb(Mg 1/3 Nb 2/3 )O 3 . Effects of the filler ratio and testing temperature and frequency on the dielectric properties of these three composites were discussed. The self-synthesized BaTiO 3 /epoxy composite had the best dielectric property. Its 40%-filler composite had a dielectric constant of 44, which was higher than those of 27 and 24 for the commercial BaTiO 3 and Pb(Mg 1/3 Nb 2/3 )O 3 composites, respectively. The large ceramic aggregates formed in epoxy are the reason for the self-synthesized BaTiO 3 /epoxy composite having a higher dielectric constant of 44. Composite dielectric constant was proportional to the volume ratio of the ceramic filler and remained constant at different test temperatures and frequencies. For the BaTiO 3 /epoxy composites, the fabrication process of slurry-casting or screen-printing can have an effect on their porosity. The composites with a low porosity lead to a low dielectric loss tangent.