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In order to study the propagation laws of acoustic wave of coal samples from the Upper Permian Xuanwei Formation in the east of Yunnan Province, China, under saturated water and dry conditions, the basic physical parameters, acoustic parameters and anisotropic parameters were obtained through the experiments. Based on FFT and wavelet analysis theory, the spectral characteristics of coal samples under different conditions were studied. The results show that physical parameters of coal samples in different directions have different values, that is, the anisotropy of coal samples is obvious. When the coal samples are saturated with water, the acoustic velocities and the attenuation coefficient increase, whereas the dominant frequency decreases. The signal amplitude of the frequency domain significantly decreases, that is, the internal structure of coal samples is damaged. The P-wave velocity and S-wave velocity increase with the increase of the confining pressure, whereas the anisotropy parameters decrease with the increase of the confining pressure. Overall, this study provides the basis to understand basic acoustic information and anisotropy characteristics of coal samples.

Background Antibody-mediated rejection (AMR) induced by donor-specific anti-human leukocyte antigen (HLA) antibodies (DSA) remains a major cause of long-term graft loss after kidney transplantation. Currently, the presence of DSA cannot always be determined at a specific allele level, because existing donor HLA typing is low resolution and often incomplete, lacking HLA-DP, and occasionally HLA-C and HLA-DQ information and historical donor DNA samples are not available for HLA retyping. Here we present a novel, non-invasive technique for obtaining donor DNA from selectively expanded donor cells from urine of renal transplant recipients. Methods Urine-derived cells were successfully expanded ex vivo from 31 of 32 enrolled renal transplant recipients, and with DNA obtained from these cells, donor HLA typing was unambiguously determined for HLA-A, -B, -C, -DRB1, -DQA1, -DQB1, -DPA1 and -DPB1 loci by next-generation sequencing. Results Our results showed 100% concordance of HLA typing data between donor peripheral blood and recipient urine-derived cells. In comparison, HLA typing showed that DNA derived from urine sediments mainly contained recipient-derived DNA. We also present the successful application of our novel technique in a clinical case of AMR in a renal transplant recipient. Conclusions Urine-derived donor cells can be isolated from kidney transplant recipients and serve as a suitable source of donor material for reliable high-resolution HLA genotyping. Thus, this approach can aid the assessment of DSA specificity to support the diagnosis of AMR as well as the evaluation of treatment efficacy in kidney transplant recipients when complete donor HLA information and donor DNA are unavailable. This article is protected by copyright. All rights reserved.

In visual reasoning, the achievement of deep learning significantly improved the accuracy of results. Image features are primarily used as input to get answers. However, the image features are too redundant to learn accurate characterizations within a limited complexity and time. While in the process of human reasoning, abstract description of an image is usually to avoid irrelevant details. Inspired by this, a higher-level representation named semantic representation is introduced. In this paper, a detailed visual reasoning model is proposed. This new model contains an image understanding model based on semantic representation, feature extraction and process model refined with watershed and u-distance method, a feature vector learning model using pyramidal pooling and residual network, and a question understanding model combining problem embedding coding method and machine translation decoding method. The feature vector could better represent the whole image instead of overly focused on specific characteristics. The model using semantic representation as input verifies that more accurate results can be obtained by introducing a high-level semantic representation. The result also shows that it is feasible and effective to introduce high-level and abstract forms of knowledge representation into deep learning tasks. This study lays a theoretical and experimental foundation for introducing different levels of knowledge representation into deep learning in the future.

A longitudinal distribution profile of the mechanical properties of the formations is important for the safe drilling, successful completion, and development of oil and gas reservoirs. However, the mechanical profile of the carbonate formations from the low-permeability gas reservoirs in the Tazhong (TZ) Block is hard to achieve due to the complex structural and lithological characteristics of the carbonates. In this paper, lab measurements are carried out to determine the physical and mechanical properties of the carbonate rocks of the Yingshan Formation in the TZ Block. Based on this, the relationships among density, the interval transit time and the mechanical parameters of the rocks in the TZ Block are constructed. The constructed relationships are then applied to the well-logging prediction of the mechanical profiles of the carbonate formations. The models are verified through the application to the two wells in the TZ Block, the results show that the relative errors in the predicted mechanical parameters are within 10% indicating the efficiency of the constructed models. The result of this study provides reasonable mechanical parameters for the exploration and development of the carbonate reservoirs in the TZ Block. Cited as : Wan, Y., Zhang, H., Liu, X., Yin, G., Xiong, J., Liang, L. Prediction of mechanical parameters for low-permeability gas reservoirs in the Tazhong Block and its applications. Advances in Geo-Energy Research, 2020, 4(2): 219-228, doi: 10.26804/ager.2020.02.10

This paper focuses on a robust continuous sliding mode control (RCSMC) method for manipulator PMSM trajectory tracking system under time-varying uncertain disturbances. The main objective of this study is to improve the trajectory tracking dynamic response and disturbances rejection ability of the manipulator PMSM, and then the manipulator itself, by using the RCSMC method. The RCSMC method consists of two key parts: a terminal continuous sliding mode controller (TCSMC), and an extended state observer (ESO). The TCSMC has been demonstrated to have remarkable capabilities to reduce the chattering phenomenon caused by high frequency switching function in the conventional sliding mode control law, and reject the strong time-varying uncertain disturbances for PMSM velocity loops. However, the high control gain, which could lead high velocity steady state fluctuations, is needed. Therefore, an extended state observer is introduced to estimate the disturbances. The estimated disturbances are further used by the velocity loop controller as a kind of feed-forward compensation to reduce the gain of the TCSMC method. The stability of the PMSM trajectory tracking system with the RCSMC algorithm is guaranteed by the Lyapunov stability criteria. Simulations have been conducted to verify the performance of the proposed RCSMC algorithm. Finally, the proposed RCSMC algorithm is applied in a practical 6-DOF manipulator, and the experimental results exhibit the extraordinary robustness and capabilities in dynamic position tracking characteristics, velocity response and disturbances rejection.

Peak detection is a crucial step in spectral signal pre-processing. The accurate locations of characteristic peaks are prerequisite for chemical identification. However, measured spectra inevitably contain both noise and baseline signals. These interference signals will generate a series of false peaks, which is a challenge for spectral analyses. For this purpose, a spectral peak detection algorithm named CWT-IS, based on continuous wavelet transform (CWT) and image segmentation (IS), is proposed. First, a wavelet coefficient matrix is obtained by CWT, then the matrix is converted to a gray image and the image is segmented by a machine vision method, an improved version of the Otsu method based on fuzzy mathematical theory. The desired threshold can be determined based on the membership grades of segmentation performance and the features of false peaks are eliminated. The fuzzy Otsu method solves the problem that the traditional Otsu method cannot effectively handle an image with a unimodal histogram, thus, this method can accurately separate peak regions from the CWT coefficient matrix and ridges representing the peaks are complete. This method has been successfully applied to the peak detection of the simulated spectra and the MALDI-TOF spectra. The experimental results show that CWT-IS can effectively eliminate the adverse effect of noise and baseline and make the features of spectral peaks become more obvious, which is conducive to determine characteristic peak positions.

Cutaneous T cell lymphoma is a generally indolent disease derived from skin-homing mature T cells. However, in advanced stages, cutaneous T cell lymphoma may manifest aggressive clinical behaviour and lead to a poor prognosis. The mechanism of disease progression in cutaneous T cell lymphoma remains unknown. This study, based on a large clinical cohort, found that IKZF2, an essential transcription factor during T cell development and differentiation, showed stage- dependent overexpression in the malignant T cells in mycosis fungoides lesions. IKZF2 is specifically over- expressed in advanced-stage mycosis fungoides lesions, and correlates with poor prognosis. Mechanistically, overexpression of IKZF2 promotes cutaneous T cell lymphoma progression via inhibiting malignant cell apoptosis and may contribute to tumour immune escape by downregulating major histocompatibility complex II molecules and up-regulating the production of anti-inflammatory cytokine interleukin-10 by malignant T cells. These results demonstrate the important role of IKZF2 in high-risk cutaneous T cell lymphoma and pave the way for future targeted therapy.

Background Lung cancer has the highest mortality rate among malignant tumors, with non‐small cell lung cancer (NSCLC) being the most common type. As the main component of the human microflora, the intestinal flora interacts with the human body to affect immunity, metabolism, and the formation of diseases. Methods Forty‐five patients with advanced NSCLC who received platinum‐containing dual‐drug chemotherapy were enrolled in a prospective exploratory cohort study. The intestinal flora was dynamically collected at baseline and after two chemotherapy cycles. Next‐generation sequencing and metagenomics were then used to analyze the species and function of the intestinal flora at all levels. Results Significant differences in the intestinal flora of patients with NSCLC were found according to sex and age. At the family level, the abundances of Streptococcaceae, Lactobacillaceae, and Leuconostocaceae after platinum‐containing dual‐drug chemotherapy were significantly higher compared to those before chemotherapy. At the family level, patients with chemotherapy‐induced gastrointestinal reactions had a significantly higher abundance of Leuconostocaceae than those without gastrointestinal responses. Meanwhile, patients with gastrointestinal reactions had higher metabolism, human diseases, cellular processes, and environmental information processing than those who did not. At the genus level, responders had higher abundances of Bacteroides compared to nonresponders. Moreover, nonresponders had higher levels of the six major metabolic pathways compared to responders. Conclusions The intestinal flora of Chinese patients with advanced NSCLC differed according to sex and age. Moreover, significant differences in the intestinal flora were noted after chemotherapy, which could be associated with chemotherapy‐induced gastrointestinal reactions and the efficacy of chemotherapy., Significant differences in the intestinal flora of patients with advanced NSCLC were found according to sex and age. The abundances of Streptococcaceae, Lactobacillaceae, and Leuconostocaceae after platinum‐containing dual‐drug chemotherapy were significantly higher compared to those before chemotherapy at the family level. Advanced NSCLC patients with gastrointestinal reactions had higher metabolism, human diseases, cellular processes, and environmental information processing than those who did not. Nonresponders had higher levels of the six major metabolic pathways compared to responders.

The influence of microbiota on host health and disease has attracted adequate attention, and gut microbiota components and microbiota-derived metabolites affect host immune homeostasis locally and systematically. Some studies have found that gut dysbiosis, disturbance of the structure and function of the gut microbiome, disrupts pulmonary immune homeostasis, thus leading to increased disease susceptibility; the gut-lung axis is the primary cross-talk for this communication. Gut dysbiosis is involved in carcinogenesis and the progression of lung cancer through genotoxicity, systemic inflammation, and defective immunosurveillance. In addition, the gut microbiome harbors the potential to be a novel biomarker for predicting sensitivity and adverse reactions to immunotherapy in patients with lung cancer. Probiotics and fecal microbiota transplantation (FMT) can enhance the efficacy and depress the toxicity of immune checkpoint inhibitors by regulating the gut microbiota. Although current studies have found that gut microbiota closely participates in the development and immunotherapy of lung cancer, the mechanisms require further investigation. Therefore, this review aims to discuss the underlying mechanisms of gut microbiota influencing carcinogenesis and immunotherapy in lung cancer and to provide new strategies for governing gut microbiota to enhance the prevention and treatment of lung cancer.

With the development of artificial intelligence, more and more people hope that computers can understand human language through natural language technology, learn to think like human beings, and finally replace human beings to complete the highly difficult tasks with cognitive ability. As the key technology of natural language understanding, sentence representation reasoning technology mainly focuses on the sentence representation method and the reasoning model. Although the performance has been improved, there are still some problems such as incomplete sentence semantic expression, lack of depth of reasoning model, and lack of interpretability of the reasoning process. In this paper, a multi-layer semantic representation network is designed for sentence representation. The multi-attention mechanism obtains the semantic information of different levels of a sentence. The word order information of the sentence is also integrated by adding the relative position mask between words to reduce the uncertainty caused by word order. Finally, the method is verified on the task of text implication recognition and emotion classification. The experimental results show that the multi-layer semantic representation network can promote sentence representation’s accuracy and comprehensiveness.

Recently, the machine learning (ML) atomic force field has emerged as a powerful atomic simulation approach because of its high accuracy, low computational cost, and transferability. In this mini review, we first summarize the disadvantages of traditional force field and the unique advantages of ML-based force field for molecular dynamics simulations. Then the basic workflow to develop the ML atomic force field is discussed in each step. Furthermore, taking carbon material as a typical example, the various applications of ML-based force fields for studying the carbon allotropic materials are reviewed. Finally, the perspectives are discussed and future directions for studying atomic force field by ML are given.

Accurate analysis of the patient's heart function, and early diagnosis of myocardial disease can improve the treatment effect and reduce the medical cost significantly. Among the different medical imaging techniques, cardiac magnetic resonance (CMR) has high tissue contrast which is widely used in clinic. However, pro-processing CMR data manually for diagnose is extremely time consuming. To develop an automatic cardiomyopathy recognition algorithm among normal group, hypertrophic cardiomyopathy, and dilated cardiomyopathy group, we employ the CNN and LSTM to extract spatial and motion features. In addition, we propose a motion based auxiliary task to help the main recognition task, without additional annotation. In experiment, compared to C3D [1] and LRCN [2], the proposed method obtains the best performance. Both accuracy and AUC score achieve 0.94.

While numerous studies have been carried out to characterize heat transport behaviours in various crystalline silicon nanostructures, the corresponding characteristics of amorphous one-dimension system have not been well understood. In this study, we amorphize crystalline silicon by means of helium-ion irradiation, enabling the formation of a completely amorphous region of well-defined length along a single silicon nanowire. Heat conduction across both amorphous region and its crystalline/amorphous interface is characterized by an electron beam heating technique with high measurement spatial resolution. The measured thermal conductivity of the amorphous silicon nanowire appears length-independence with length ranging from ~30 nm to few hundreds nm, revealing the fully diffusons governed heat conduction. Moreover, unlike the size-dependent interfacial thermal conductance at the interface between two one-dimensional crystalline materials, here for the first time, we observe that the interface thermal conductance across the amorphous/crystalline silicon interface is nearly independent of the length of the amorphous region. This unusual independence is further supported by molecular dynamics (MD) simulation in our work. Our results provide experimental and theoretical insight into the nature of interaction between heat carriers in crystalline and amorphous nano-structures and shed new light to design innovative silicon nanowire based devices.

Small sample learning aims to learn information about object categories from a single or a few training samples. This learning style is crucial for deep learning methods based on large amounts of data. The deep learning method can solve small sample learning through the idea of meta-learning “how to learn by using previous experience.” Therefore, this paper takes image classification as the research object to study how meta-learning quickly learns from a small number of sample images. The main contents are as follows: After considering the distribution difference of data sets on the generalization performance of measurement learning and the advantages of optimizing the initial characterization method, this paper adds the model-independent meta-learning algorithm and designs a multi-scale meta-relational network. First, the idea of META-SGD is adopted, and the inner learning rate is taken as the learning vector and model parameter to learn together. Secondly, in the meta-training process, the model-independent meta-learning algorithm is used to find the optimal parameters of the model. The inner gradient iteration is canceled in the process of meta-validation and meta-test. The experimental results show that the multi-scale meta-relational network makes the learned measurement have stronger generalization ability, which further improves the classification accuracy on the benchmark set and avoids the need for fine-tuning of the model-independent meta-learning algorithm.

In view of the complex causes of flood disasters at the grass-roots level in the plains and the difficulty of defense, this study proposes a point-to-surface flood disaster survey and evaluation method in the plains: First, the villages in the plains are divided into low-lying villages based on the causes of the disaster. And the villages affected by river floods; secondly, the two types of villages are used as the unit to investigate the basic situation of village flood disasters, starting from the three-level flood control and drainage system of the drainage system in the village, the main drainage canal outside the village, and the middle and small rivers to complete the investigation of the flood disaster; The calculation methods of the village’s critical rainfall index, critical rainfall index and river flood control capacity are put forward to quantitatively assess the village’s flood disaster risk and engineering flood control and drainage capacity; finally, corresponding countermeasures are proposed from the engineering and non-engineering perspectives based on the investigation situation, which is for plain areas. Flood disaster prevention work provides a reference.

ObjectivesTo develop a model of in-hospital mortality using medical record front page (MRFP) data and assess its validity in case-mix standardisation by comparison with a model developed using the complete medical record data.DesignA nationally representative retrospective study.SettingRepresentative hospitals in China, covering 161 hospitals in modelling cohort and 156 hospitals in validation cohort.ParticipantsRepresentative patients admitted for acute myocardial infarction. 8370 patients in modelling cohort and 9704 patients in validation cohort.Primary outcome measuresIn-hospital mortality, which was defined explicitly as death that occurred during hospitalisation, and the hospital-level risk standardised mortality rate (RSMR).ResultsA total of 14 variables were included in the model predicting in-hospital mortality based on MRFP data, with the area under receiver operating characteristic curve of 0.78 among modelling cohort and 0.79 among validation cohort. The median of absolute difference between the hospital RSMR predicted by hierarchical generalised linear models established based on MRFP data and complete medical record data, which was built as ‘reference model’, was 0.08% (10th and 90th percentiles: −1.8% and 1.6%). In the regression model comparing the RSMR between two models, the slope and intercept of the regression equation is 0.90 and 0.007 in modelling cohort, while 0.85 and 0.010 in validation cohort, which indicated that the evaluation capability from two models were very similar.ConclusionsThe models based on MRFP data showed good discrimination and calibration capability, as well as similar risk prediction effect in comparison with the model based on complete medical record data, which proved that MRFP data could be suitable for risk adjustment in hospital performance measurement.

The Enhanced Super-Resolution Generative Adver-sarial Networks (ESRGAN) is the state of the art deep learning based image Super-Resolution (SR) and has the best performance in perceptual quality. However, we find it is time-consuming, which makes it impractical for SR at clients' side during video delivery since SR usually uses clients' computing resources (the computational power at the clients' side should not be as powerful as GPU) and videos often require real-time playback. While Efficient Sub-Pixel Convolutional Neural Network (ESPCN) has the best real-time performance, it is still not capable of offering a smooth watching experience and has much lower perceptual quality. In order to simultaneously meet the demands on the real-time performance and the resulting pleasant artifacts of SR at the clients' side, we propose RTSRGAN to exploit the advantages of ESRGAN in image perceptual quality and ESPCN in real-time performance. Our experimental results indicate that RTSRGAN has the fastest reconstruction speed, on the average, 15 images per second on a single 2.3GHz CPU (only 6 images per second by ESPCN), and reconstructs images of a relatively acceptable perceptual quality, which validates that our proposed RTSRGAN can be used for SR at clients' side to enhance the real-time performance and ensure the image perceptual quality.

BackgroundHigh salt consumption has contributed to the rise of noncommunicable diseases around the world. The application of mobile health (mHealth) technologies has witnessed rapid growth in recent years. However, evidence to support mHealth interventions to confront the challenge of salt reduction has not yet been critically reviewed. ObjectiveThe aim of this study was to identify, characterize, and evaluate mHealth interventions aimed at salt reduction across the world. MethodsA systematic search of studies in English or Chinese language published from January 1, 1992 to July 31, 2017 was conducted using 4 English databases (PubMed, MEDLINE, Global Health, and Cochrane) and 3 Chinese databases (Wanfang, China Science and Technology Journal, and China National Knowledge of Infrastructure). All studies directly using mobile technologies in health care with a primary or secondary objective of reducing dietary salt consumption were included. ResultsA total of 1609 articles were found using the search strategy, with 11 full articles (8 English and 3 Chinese) being included for data extraction, including 11 interventional studies. Overall, few high-quality interventions were identified. Most interventions were limited by small study population sample sizes, lack of control groups, and short follow-up times, all of which were obstacles in generating long-term scalable approaches. Most interventions employed short message service as a platform for mHealth interventions, whereas some innovative mHealth technologies were also explored. Most interventions had a primary focus of improving awareness of dietary salt consumption. The outcome variables used to measure intervention effectiveness included 24-hour urinary sodium excretion, spot urine sampling, dietary records, and indirect behavior or knowledge indicators targeting salt consumption. Although most interventions displayed positive outcome results, none of them provided reliable evidence to evaluate the effectiveness of salt reduction. ConclusionsSalt reduction in mHealth initiatives remains relatively unexplored; however, studies that did intervene on salt-reduction show the potential of mHealth as an effective intervention method. We provide 3 recommendations for future mHealth interventions in salt reduction—(1) increased use of new, innovative, and interactive mHealth technologies; (2) development of mHealth interventions with primary prevention measures and goals of salt reduction; and (3) large-scale, rigorously designed, and object-targeted clinical trials of mHealth interventions with appropriate quantitative outcome variables, in particular 24-hour urine sodium.

Image super-resolution is an essential technology for improving user quality of experience of internet videos. As the state-of-the-art deep learning-based super-resolution technology, the enhanced super-resolution generative adversarial networks (ESRGAN) has the best performance in the perceptual quality of reconstructed images, and the efficient sub-pixel convolutional neural network (ESPCN) has the best real-time performance. This work proposes real-time super-resolution generative adversarial network (RTSRGAN). RTSRGAN takes the advantages of ESRGAN and ESPCN so as to simultaneously satisfy the demands on the real-time performance and the resulting pleasant artifacts of super-resolution at the client side. Our experimental studies demonstrate our proposed RTSRGAN can be used for super-resolution at the client side to enhance the real-time performance as well as ensure the image perceptual quality.We also find that RTSRGAN is suitable for restoring imageswith regularly changing texture featureswithout requiring training for individual image categories.

In this paper, the core shale samples from the Lower Longmaxi Formation (LF) in the southwest of the Sichuan Basin of China are carried out laboratory experiments to investigate the wettability and adsorption characteristics. The influences of the wettability on the development of the shale gas reservoirs were discussed. And the influences of the kerogen and the pure mineral on the methane adsorption capacity were discussed. The differences between the methane adsorption capacity of the pure mineral and the mixed minerals were also discussed. The results show that the spontaneous imbibition rate of the LF shale samples tended to rise firstly and then become stable with the increasing of the time; The spontaneous imbibition rate of water was higher than the spontaneous imbibition rate of oil; The methane adsorption capacity on the kerogen and the pure minerals first increased rapidly and then increased slowly and tended to be stable with the increasing of the pressure; The methane adsorption capacity on the kerogen was much larger than the different pure minerals; And among the different pure minerals, the order of the methane adsorption capacity is that: illite > chlorite > quartz; The water molecules would reduce the methane adsorption capacity on the Kerogen; The methane adsorption capacity of the mixed minerals can be approximately equal to the sum of the methane adsorption capacity of the pure minerals calculated by the mass ratios. The water blocking damage in the shale formation can’t be assessed according to the water blocking damage evaluation system of the conventional oil and gas reservoirs. The mixed wettability, the influence of the water on the methane adsorption capacity and water-induced cracks should be considered in the water blocking damage evaluation system of the shale reservoir.

Horizontal well has gained increasing importance in oil and gas development. Thus stress distribution models around the horizontal well attract more people than earlier. In the last few years, a lot of stress distribution models have been proposed by domestic and international scholars. Through numerical simulation analysis, the differences between five models have been analyzed. The classic model suits for qualitative characterization. Using D-P principle to judge well wall stability of different models instructs that the differences between models decrease with the increase of stability factor.

This article presents a novel compact tunable bandpass filter (BPF) with center frequency and bandwidth control based on open-circuited stub-loaded half-wavelength resonator (SLR). Analyses of frequency and bandwidth tunable of the proposed resonators and external quality factor implemented by tunable J inverter to gain a stable filtering performance are discussed. Both theory and experiment are provided to validate the proposed filter. From the experimental results, it is found that the proposed filter can keep a constant bandwidth over the frequency tuning range with an insertion loss of less than 0.9 dB and increase the 1-dB bandwidth from 120 MHz to 430 MHz when center frequency locates at 1.3 GHz with an insertion loss of 0.5–1.7 dB. Simulation and measurements are in excellent agreement. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:803–806, 2016

Fractures have a significant effect on the mechanical properties of coal and rock, and fracture structure is complex. Combinined with indoor physical tests and corresponding numerical simulation tests, the relationships among rock mechanics parameters, cleat complexity, and wave velocity were analysed. Based on the conversion relationships between coal and rock cleat complexity and coal seam fissure complexity, four calculation models of rock mechanics parameters based on coal bed fracture complexity and wave velocity were established. The results indicate that coal with different cleat density, quantity, or angle has noticeable fractal characteristics, and cleat fractal dimension can be used to characterize the cleat complexity. The fractal dimension of deep lateral resistivity curve can characterize the fracture complexity of coal beds. There is a good linear positive correlation between cleat fractal dimension and fractal dimension of deep lateral resistivity logging curve. Velocity and cleat complexity have no significant correlation, and the attenuation coefficient increases with the increase in cleat complexity. Uniaxial compressive strength of coal and rock decrease with the increase in cleat complexity and is more sensitive to the change in cleat angle than to cleat density and cleat quantity changes. Young's modulus of coal and rock decrease with the increase in cleat complexity and is more sensitive to the change in cleat quantity than cleat density or angle changes. Poisson's ratio of coal increase with the increase in cleat complexity and is more sensitive to the change in cleat quantity than cleat density or angle changes. The tensile strength of coal and rock increases with the increase in cleat complexity and is more sensitive to the change of cleat quantity than cleat density and angle changes. Centralized distribution of cleats has more significant influence on the uniaxial compressive strength, Young's modulus, Poisson's ratio, and tensile strength of coal than an even distribution. The calculation models of rock mechanics parameters based on fracture complexity are suitable for the study area. The method of combining the fracture complexity of coal beds with acoustic wave velocity to calculate rock mechanical parameters are feasible.

A very compact triple-band antenna with rectangular ring for wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications is designed in this article. The antenna has a simple structure and it consists of a rectangular ring, a fork-shaped strip, a modified 1-shaped stub, and a coplanar waveguide(CPW) which induces proposed antenna has the advantage of simple structure with a single metallic level, and it is easier to integrate into the microwave integrated circuit. It can be adjusted the position and length of these structures to achieve the three-different frequency and bandwidth, respectively. As a result, the triple-band antenna has been designed, and simulated, and then obtaining a high antenna performance.

A pore network model could represent the porous medium space to predict rocks' various flow properties. This paper proposes a new method via genetic algorithm (GA) to reconstruct the pore network model with irregular distribution and topologically equivalent based on the real porous medium. Firstly, the properties of pore and throat are based on the Weilbull distribution function. A random pore network model is built by some assumptions and Weilbell parameters. In order to obtain the reliable parameters of pore and throat, the Avizo Fire analysis software is used to analysis the sample's micro-computed tomography (micro-CT) images. The theory of pore network simulation also is introduced simply in this section. Second, two objective functions for GA are given. We build up an objective function to reduce the discrepancy of slices of numerical model with the real micro-CT images, which is controlled by the core porosity. The second objective function makes model much more agreed with the experiment value. Thirdly, according to the characteristics of control parameters, we choose three steps alternating optimization to construct the model via GA. It demonstrates that this method has a quick convergence speed than before experiments. We adopt two kinds of complex geological porous medium to test the validity of our proposed method. One is a rock with high porosity (Sample One). The other is the dense sandstone (Sample Two). After the optimization, two appropriate pore network models are performed excellent of the prediction displacement in primary oil flooding of the target porous medium.

By way of a quantitative study of the patterns of thematic progression of the argumentative essay ‘Of Studies’ and its 11 Chinese translations, we attempt to demonstrate the degree of relevance of these patterns to translation studies. Our study reveals that these patterns and their shifts in translation display certain regularity in their frequency and density, which in turn may shed light on many issues in the English–Chinese translation of argumentative classics, such as the constitution of the ‘interlingua’ and the degree of correspondence between the source text and the target text(s).

The Power Integrated Automation System has a large amount of the real-time data, it needs to achieve data sharing in different modules in its own system, sometimes even needs to be shared with the other systems. The thesis discusses the characteristics and the ways of the real-time data sharing in the first place. Then, it compares the merits and drawbacks in different ways. Besides, it gives a viable resolution in different aspects, such as the design of the real-time database, the framework of the communication system and the design of the communication software, as well as the real-time data sharing in different systems.

Market-oriented reverse auction is an efficient and cost-effective method for resource allocation in cloud workflow systems since it can dynamically allocate resources depending on the supply-demand relationship of the cloud market. However, during the auction the price of cloud resource is usually fixed, and the current resource allocation mechanisms cannot adapt to the changeable market properly which results in the low efficiency of resource utilization. To address such a problem, a dynamic pricing reverse auction-based resource allocation mechanism is proposed. During the auction, resource providers can change prices according to the trading situation so that our novel mechanism can increase the chances of making a deal and improve efficiency of resource utilization. In addition, resource providers can improve their competitiveness in the market by lowering prices, and thus users can obtain cheaper resources in shorter time which would decrease monetary cost and completion time for workflow execution. Experiments with different situations and problem sizes are conducted for dynamic pricing-based allocation mechanism (DPAM) on resource utilization and the measurement of Time⁎Cost (TC). The results show that our DPAM can outperform its representative in resource utilization, monetary cost, and completion time and also obtain the optimal price reduction rates.

Compact planar crossovers with single and dual band application are proposed. The crossovers are derived from the conventional two-section branch-line crossover by equivalent quarter-wavelength transmission-line with stubs, which reduces the circuit size and complexity. The crossover geometries and design procedure are discussed in detail according to the derived formulas. For verification, a single-band crossover working at 2.4 GHz and a dual-band crossover applied at 2.2/3.4 GHz are fabricated and tested. The proposed crossovers feature planar implementation with moderate line impedance, compact size and good performance. Both simulated and measured results are consistent with each other.

In order to ensure that SAR scene matching aided navigation system can acquire the position errors and yawing errors simultaneously, we propose an image matching algorithm based on Scale Invariant Feature Transform (SIFT). However, the SIFT is proposed for optical image, and its performance degrades when used in SAR image. To enhance the adaptability of SIFT, two ways are employed. One is the application of a preprocessing on image pairs before matching. The other is the establishment of a scale and rotation restriction criteria on tie-points after SIFT matching. Compared with other matching methods, experiment results show that the proposed method is much more suitable for SAR image and successes in matching performance improvement. Furthermore, the method can meet the real-time requirement.

The lubricant, perfluoropolyethers (PFPEs), has been modeled and analyzed using molecular dynamics simulations based on a coarse-grained bead-spring model. An ultrathin lubricant film with a thickness of 1-2 nm is coated on a disk to lubricate the head disk interface (HDI) of hard disk drives (HDDs). The retention performance of the lubricant film is studied, which is important to avoid a direct contact between the head and disk. The replenishment performance is also studied, which is essential to repair the lubricant film ruptured by a contact of the head. Finally, the typical phenomenon of touch down-take off hysteresis during the contact between head and disk is confirmed and analyzed because of the nanoscopic interactions in the HDI.

The association of two cholesterol ester transfer protein (CETP) polymorphisms, D442G and TAQIB (B1-->B2), with high-density lipoprotein (HDL) levels in 932 Chinese obese individuals (BMI >or= 27) was investigated in comparison with normal controls (BMI

In the thermal-assisted data storage technologies, the behavior of heat transfer between the nano-tips and the storage medium during thermo-mechanical data bit formation process is a critical factor affecting the areal storage density, data bit writing/reading speed and system reliability. In this paper, the thermal properties of a nano-tip are analyzed using the non-equilibrium molecular dynamics simulation. The simulated results show that the effects of the nano-structural configuration and boundary conditions on the thermal transport are remarkable, which can be attributed to the phonon boundary-scattering and possible phonon spectrum modification. Furthermore, the heat transfer between the nano-tip and the silicon medium film is simulated. The results show that the medium film can be efficiently heated locally with no pressure force. For a tip-medium contact area of 5.31 nm2, an area of about 95.5 nm2 on the medium surface can be heated with a temporal resolution of 0.11 ns. This time period is much s...

In this article, a novel particle swarm optimization method based on IE3D is used to design a low-profile modified U-slot microstrip antenna. The antenna is printed on a substrate thickness of 0.008λ. Details of the particle swarm optimization method based on IE3D are presented. Experimental results including return loss and far field patterns of the antenna are given. The obtained antenna bandwidth (S11 = −10 dB) can be as large as 1.85 times that of a corresponding unslotted rectangular microstrip antenna at 2.4 GHz. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 1111–1114, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI.10.1002/mop.22369

The reverse auction method is widely applied to resource allocation for cloud workflow systems, and it dynamically allocates resources depending on the supply and demand of market. However, during the auction the price of cloud resource is usually fixed, and the resource allocation mechanism cannot adapt to the changeful market. This results in low efficiency of resource utility. In this paper, we first propose a dynamic pricing strategy in reverse auction, and then present an efficient resource allocation mechanism based on dynamic pricing reverse auction. During the auction, providers change resource price according to the trade situation so that the novel mechanism can increase chances of making a deal and improve efficiency of resource utility. In addition, providers improve their competitiveness by lowering price. Simultaneously users are timelier to get resources with the increasing of trade rate. Therefore, users can obtain cheaper resources in shorter time, which decrease monetary cost and completion time for workflow execution. The experiments show that our mechanism can achieve better results in resource utility, monetary cost and completion time compared to BOSS algorithm.

This paper proposes a new integrative system (GNBSL--Gram-negative bacteria subcellular localization) for subcellular localization specifized on the Gram-negative bacteria proteins. First, the system generates a position-specific frequency matrix (PSFM) and a position-specific scoring matrix (PSSM) for each protein sequence by searching the Swiss-Prot database. Then different features are extracted by four modules from the PSFM and the PSSM. The features include whole-sequence amino acid composition, N- and C-terminus amino acid composition, dipeptide composition, and segment composition. Four probabilistic neural network (PNN) classifiers are used to classify these modules. To further improve the performance, two modules trained by support vector machine (SVM) are added in this system. One module extracts the residue-couple distribution from the amino acid sequence and the other module applies a pairwise profile alignment kernel to measure the local similarity between every two sequences. Finally, an additional SVM is used to fuse the outputs from the six modules. Test on a benchmark dataset shows that the overall success rate of GNBSL is higher than those of PSORT-B, CELLO, and PSLpred. A web server GNBSL can be visited from http://166.111.24.5/webtools/GNBSL/index.htm.

As one of market-oriented distributed systems, cloud computing has become popular in recent years. In scientific cloud workflows, scheduling optimization is an important and challenging issue, especially at the task level. Traditional approaches do not consider the time-sharing characteristic of virtual machine (VM), and they cannot reduce the makespan of scheduler within a datacenter effectively. However VM can execute multiple parallel tasks simultaneously. In this paper, with consideration of cost constraint, we present novel efficient task-level Ant Colony Optimization scheduling based on time-sharing VM. The experimental simulation shows that our scheduling can make better use of time-sharing VM to optimize the makespan of scheduler within a datacenter. Therefore, our scheduling can run tasks more efficiently.

Overexpression of erbB-2 proto-oncogene has been found in 20%-30% of human breast carcinomas and in most cases correlates with poor clinical prognosis. Using antisense oligonucleotides targeted to the 5' cap region of erbB-2RNA, we were able to inhibit erbB-2 protein expression, proliferation, and anchorage-independent growth of breast cancer cells up to 90%. These effects were sequence specific and restricted to cells expressing elevated level of erbB-2 protein. These support the feasibility of using antisense erbB-2 oligonucleotides to inhibit the progression of erbB-2-overexpressing breast cancer cells.

The feature extraction was analyzed and matching algorithms of object tracking, then we develop an object tracking method based on local feature matching in this paper. First we introduce about the existing feature extraction algorithms and the basic idea of Shape Context. then we propose the tracking algorithm based on local characteristics. in this algorithm, we evaluate the similarity between the two feature points by histogram distance statistics based on ¦O2-test and we determine the tracking trajectory by votes and prediction to the matching points. It is better matched in target deformation, rotation and occlusion by using this method. the experiment results show that our object matching method is effective.

This paper based on the RF chip of nRF24LE1 achieves the design of electronic license plate (E-Plate) recognition terminal system. Hardware and software design and implementation of this system is given. the 32-bit high performance processor of LM3S8962 of TI Company used as the main controller to achieve communication with the RF chip, the high performance can effectively improve the performance of the reader. at the same time, the implementation of Low-power design was achieved in the software to ensure the electronic tag can work stably for a long time.

An novel object detection and segmentation algorithm is proposed based on contour information and inference. The scheme proceeds in mo stages, in the first stage, some prototypes are learned automatically from their 3D models and training images. Firstly, aiming to viewpoint invariance, a two-layer structural of multi-views is built, secondly, the corner-polygon (CP), which is combination of 3 features, is proposed for describing templates. In the second stage, the generated framework is used for detecting category-given object. A new Dynamic Bayesian network (DEN) model is built to infer detection probability, based on corresponding patch matching and inference computing, the candidate window s should be found out, then thin plate spline (TPS) technique is used to segment object from background. Experimental results demonstrate that our proposed approach is able to identify and accurately detect and segment the objects with better performance than the existing methods.

There is an increasing interest in the identification and quantification of proteomic markers for biological and medical research. Experimental techniques utilizing liquid chromatography with mass spectrometry (LC- MS) provide one method of directly observing protein expression, but a substantial amount of computational work is needed to go from the raw LC-MS data to a matrix format analogous to gene expression studies in which the columns are samples and rows are proteins. One critical step in this pipeline is the extraction of peptide features from the LC-MS signal data. We present a complete solution to LC-MS feature detection that combines a model-based approach to feature extraction on the MS scans with techniques for robust estimation to build LC-MS features from the individual scans. We show that using our approach, we find significantly more features, more matches, and better correlation between replicated LC-MS experiments than are found using the current state-of-the-art software.

A novel nanopositioning stage was designed according to the scanning property of SPM with flexure hinge as kinematic structure and piezoelectric ceramic as actuator. Kinetic precision and X directional area of nanopositioner are 1.55nm and 26.4 micron, respectively, which is demonstrated by kinetic analysis and finite element method FEM simulation. Designed nanopositioner based on SPM moves at 3 dimensions with nanometer scale and its motion of X, Y, and Z directions is decoupled and isotropic. Furthermore, frame of nanopositioner is simple and manufacturing is convenient, which will have broad prospect in the field of nanopositioning and nanotracing.

Background Cytopenia beyond day 28 post-transplant (CB-28PT) following hematopoietic stem cell transplantation (HSCT) with β-thalassemia major (TM) rarely was reported. The exact mechanism for the development of CB-28PT is not well known. Aim To find out causes of CB-28PT cytopenia. Method We retrospectively analyzed data (HLA mismatch status, HLA antibody status of patients, KIR gene mismatch status, KIR-ligand matching status, donor/patient CMV status, donor/patient age and sex) of 93 TM patients underwent HLA 8/8 fully matched or 7/8 matched unrelated donor HSCT. All the patients used sole NF-08-TM protocol with median follow-up time of 19 (r: 2-44) months. Results Results show a significant association between DRB1 mismatch and CB-28PT (P = 0.012). In addition, presence of Class I HLA antibody in the patient’s sera seems increase the chance of CB-28PT. Finally, the matching between inhibitory KIR2DL1 and their corresponding ligand HLA-C2 has a protective effect for CB-28PT. Conclusion We propose that CB-28PT may be a primary manifestation of cGVHD in pediatric TM patients undergoing HSCT positive influenced by HLA DRB1 mismatch, HLA class I antibody and negatively affected by KIR-ligand match. Disclosures No relevant conflicts of interest to declare.