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Subxiphoid thymectomy with double elevation of the sternum is a novel minimally invasive method that might provide a good surgical view and space for operating.1,2 Here we report the use of this approach to manage a Masaoka stage III thymoma with invasion to the edge of the innominate vein, which proved safe and feasible

Adipose-derived stem cells (ADSCs) have been shown to be beneficial in some pulmonary diseases, and the paracrine effect is the major mechanism underlying ADSC-based therapy. Autophagy plays a crucial role in maintaining stem cell homeostasis and survival. However, the role of autophagy in mediating ADSC paracrine effects has not been thoroughly elucidated. We examined whether ADSCs participate in lipopolysaccharide (LPS)-induced pulmonary microvascular endothelial cell (PMVEC) barrier damage in a paracrine manner and illuminated the role of autophagy in regulating ADSC paracrine effects. PMVECs and ADSCs with or without autophagy inhibition were cocultured without intercellular contact, and the microvascular barrier function was assessed after LPS treatment. ADSC paracrine function was evaluated by detecting essential growth factors for endothelial cells. For in vivo experiments, ADSCs with or without autophagy inhibition were transplanted into LPS-induced lung-injury mice, and lung injury was assessed. ADSCs significantly alleviated LPS-induced microvascular barrier injury. In addition, ADSC paracrine levels of VEGF, FGF, and EGF were induced by LPS treatment, especially in the coculture condition. Inhibiting autophagy weakened the paracrine function and the protective effects of ADSCs on microvascular barrier injury. Moreover, ADSC transplantation alleviated LPS-induced lung injury, and inhibiting autophagy markedly weakened the therapeutic effect of ADSCs on lung injury. Together, these findings show that ADSC paracrine effects play a vital protective role in LPS-induced pulmonary microvascular barrier injury. Autophagy is a positive mediating factor in the paracrine process. These results are helpful for illuminating the role and mechanism of ADSC paracrine effects and developing effective therapies in acute lung injury.

Capsule network (CapsNet) can recognize the objects by encoding the part-whole relationships in a way similar to our human perceptual system and has already shown its great potential in image classification tasks. However, it is limited to the real domain while the complex numbers having much richer representational capacity and facilitating the noise-robust memory retrieval mechanisms. Therefore, we propose two architectures: Complex-valued Dense CapsNet (Cv-CapsNet) and Complex-valued Diverse CapsNet (Cv-CapsNet++), each of them consists of three stages. In the first stage, multi-scale complex-valued features are obtained by the restricted dense complex-valued subnetwork. Particularly, Cv-CapsNet++ utilizes a three-level Cv-CapsNet hierarchical model to extract the multi-scale high-level complex-valued features in order to adapt to the complicated datasets. In the second stage, these complex-valued features are encoded into the complex-valued primary capsules, Particularly, Cv-CapsNet++ encodes the complex-valued features from different hierarchies into the multi-dimensional complex-valued primary capsules. In the third stage, we generalize the dynamic routing algorithm to the complex-valued domain and employ it to fuse the real- and imaginary-valued information of complex-valued primary capsules. The experimental results show that the proposed architectures lead to fewer trainable parameters, better performance, and fewer iterations during training than Real-valued CapsNets (Rv-CapsNets) with similar structure and original CapsNet on FashionMNIST and CIFAR10 datasets.

On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly-based, link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on the datasets of road traffic monitoring in the Beijing-Tianjin-Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than a Gaussian process regression (GPR) model. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. One notable finding is that NOX, fine particulate matter (PM2.5) and black carbon (BC) emissions from heavy-duty trucks (HDTs) in general have higher emission intensity on the highways connecting to regional ports. Even when traffic restrictions were implemented, a detour of the HDTs in Hebei was observed resulting in relatively lower emission reductions in Hebei than Beijing. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, which provides a platform to realize the near real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.

With the increasing scale of new energy access to the power grid, UHVDC transmission has become an important way for its consumption. At the same time, THE fault of UHVDC causes transient overvoltage in the power grid of the new energy delivery end, which causes the off-grid of new energy and seriously limits the UHVDC transmission capacity, which becomes an urgent problem to be solved. To this end, this paper proposes a new kind of dc power supply sending power grid transient overvoltage of distributed control strategy, first analyzes the UHVDC fault of new energy sending power grid transient overvoltage mechanism, based on this, put forward control of transient overvoltage UHVDC rectifier station and the sending power grid of distributed control framework, and based on dc fault transient overvoltage of the new energy sending grid space impact characteristics, puts forward the sending power grid transient overvoltage of distributed control strategy. Finally, simulation results show the effectiveness of the control strategy.

As one key content of future grid development, the methodological rationality of power grid planning has crucial impact on the operational security and stability for the power grid. With rapid development of wind power and photovoltaics, the wind-solar-coal bundled transmission pattern is becoming an inevitable trend in order to balance the mismatch between renewable energy generation and load demand in China, which has caused great challenges to power gird planning. In order to address the uncertainty of wind power generation, a two-level planning model considering N-1 security constraint is established in this paper which divides the grid planning problems into four typical scenarios considering the seasonal characteristics of wind power outputs. The high-level model, comprehensively considering the operational efficiency, aims to minimize the costs of wind-solar-coal bundled power transmission under various operating conditions while the low-level model aims to minimize the load shedding based on N-1 security analysis. Both the high-level and low-level models take N-1 constraint as the primary constraint and the best planning scheme can be obtained through iterations. According to the given planning scheme and the actual operation scenarios, the final scheme can be optimized and selected.

To mitigate car traffic problems, the United Nations Human Settlements Programme (UN-Habitat) issued a document that provides guidelines for sustainable development and the promotion of public transport. The efficiency of the policies and strategies needs to be evaluated to improve the performance of public transportation networks. To assess the performance of a public transport network, it is first necessary to select evaluation criteria. Based on existing indicators, this research proposes a public transport criteria matrix that includes the basic public transport infrastructure level, public transport service level, economic benefit level, and sustainable development level. A public transport criteria matrix AHP model is established to assess the performance of public transport networks. The established model selects appropriate evaluation criteria based on existing performance standards. It is applied to study the Stonnington, Bayswater, and Cockburn public transport network, representing a series of land use and transport policy backgrounds. The local public transport authorities can apply the established transport criteria matrix AHP model to monitor the performance of a public transport network and provide guidance for its improvement.

The lateral resolutions of electrostatic force microscopy (EFM) are systematically simulated using the boundary element method, considering a bimetallic sample with surface potential inhomogeneitie...

Increasing knowledge of long non-coding RNAs (lncRNAs) has shown that they can be used as circulating tumor markers. Also, considerable evidences have revealed that lncRNAs have important roles in tumor diagnosis and prognosis. The lncRNA CCAT2 has manifested its carcinogenic effect in a variety of tumors, but the serum expression level and clinical value in multiple myeloma (MM) remain to be explored. In our study, the expression of lncRNA CCAT2 is upregulated in the serum and bone marrow of MM patients by using quantitative real-time polymerase chain reaction (qRT-PCR). The high expression level of CCAT2 in the serum of MM patients correlated with International Scoring System (ISS) stages, renal dysfunction, serum β2-microglobulin (β2-MG) concentration, and light chain (κ and λ) concentrations. Area under the curve (AUC) of CCAT2 in serum is 0.899. Besides, the sensitivity and specificity were 85.80% and 83%, respectively. Furthermore, combination of CCAT2, IgA, HGB, and β2-MG significantly improved the MM diagnostic sensitivity and AUC. Here, our present investigation indicates that serum circulating CCAT2 may serve as a potential tumor marker for diagnosis and prognosis of MM.

Since agricultural cooperatives have developed rapidly under the farmland transfer policy in China, they play an important role in the new operation pattern for China's fresh agricultural product supply chains. To enhance current agricultural supply chains' stability, we consider a three-level (farmer-cooperative-retailer) fresh agricultural product supply chain, conduct quantitative analysis of the impact of the quantity flexibility contract, and compare the impact of the relational contract with that of the quantity flexibility contract on the freshness and the profit. Our results show that a suitable relational contract can improve the freshness and increase the profit of the three-level supply chain, but cannot fully guarantee its stability. Furthermore, the government's subsidy policy can improve the relational contract stability of the fresh agricultural product supply chain by providing the protection price contract mechanism of the agricultural product and the cold chain facility subsidy contract.

A combined fuzzy based feedforward (FBF) and bubble size distribution (BSD) based feedback reagent dosage control strategy is proposed to implement the product indices in copper roughing process. A fuzzy theory based feedforward compensator will be used to calculated the reagent dosage in advance to eliminate the influence of large disturbances according to ore grade and handling capacity. Since the bubble size is believed to be closely related to flotation performance and responds to changes in the reagent dosage, using BSD based feedback predictive control calculates the reagent dosage to stabilize the flotation running. Instead of simple statistic feature, the bubble size with non-Gaussian feature is characterized to be probability density function (PDF) by using B-spline. A multi-output least square support vector machine (MLS-SVM) based is then applied to establish a dynamical relationship between the weights of B-spline and the reagent dosage since the weights are interrelated and related to the reagent dosage. A multiple step based optimization algorithm is finally proposed to determine the reagent dosage. Experimental results can show the effectiveness of the proposed method.

Electrostatic force microscopy (EFM) including its derivative, Kelvin probe force microscopy, is widely used to investigate mesoscopic/nanoscopic conducting/semiconducting structures and electrical interactions. However, the resolution of EFM in ambient is largely limited by the applicable force-detection techniques. This work demonstrates that the lateral resolution of ambient EFM can be significantly improved using an intermittent contact method in conjunction with a resonant multi-frequency method for electrostatic force detection. A lateral resolution of sub-10 nm is obtained. The high-resolution contrast reflects the variation of surface potential in sample on a nanoscale. The mechanism, which is attributed to the decreased effective tip-sample distance, is elucidated by theoretical analysis. The newly developed technique can be easily integrated with existing techniques to herald the next generation of high-resolution probe techniques for various nanoscopic applications.

The thermal treatment of aluminum alloy workpieces requires strict temperature uniformity in large-scale vertical quench furnaces. To achieve the desired temperature uniformity in a large-scale spatial setting, a temperature uniformity control strategy combining workpiece temperature compensation and intelligent proportional–integral–derivative (PID) decoupling control is presented. The temperature compensation of the workpiece is realized by establishing an air heat conduction model. Moreover, an intelligent PID decoupling control system based on a novel self-growing radial basis function neural network (SGRBFNN) is developed to eliminate the strong coupling effects of multiheating zones. SGRBFNN, with the structure being dynamically adjusted by a hybrid semifuzzy Gustafson–Kessel clustering algorithm, is proposed to realize the online tuning of the parameters of the PID controller. Both the simulation and industrial experiment results demonstrate that the proposed temperature control system can effectively achieve smooth regulations and significantly improve temperature uniformity. The application also demonstrates the validity and better control performance of the proposed system compared with conventional control systems.

When the technique of boosting regression is applied to near-infrared spectroscopy, the full spectrum of samples are generally used to perform partial least squares (PLS) modeling. However, there is a large amount of redundant information and noise contained in the full spectrum. This not only increases the complexity of the model, but also reduces its predictive performance. In addition, the boosting method is sensitive to data noise. When the data are mixed with too much noise, the generalization performance of boosting will decrease, and the prediction error and the variance of PLS will be relatively large. To solve these problems, a gradient descent boosting ensemble method combined with backward interval PLS (GD-Boosting-BiPLS) is proposed in this paper. BiPLS is used to select the effective variables for the boosting base model, and each base model is trained sequentially by resampling. The spectral segmentation parameter of BiPLS and the iteration parameter of boosting are fused, and the weight of each base model is distributed by the gradient descent strategy. This leads to a new ensemble model (forward additive model) in the direction of reduced residuals. The final model is the ensemble model that obtains the minimum root mean square error of prediction (RMSEP). The proposed method is applied to the quantitative prediction of ethanol concentrations. Over iterations 1-50, the average correlation coefficients of the calibration and validation sets are 0.9628 and 0.9388, and the average RMSE of cross-validation and RMSEP are 0.0732 and 0.0675, respectively. The overall performance of the proposed GD-Boosting-BiPLS method is compared with those of various ensemble strategies and 4 kinds of state-of-the-art spectral modeling methods. The experimental results reveal that the proposed method has the best generalization performance and stability. HighlightsAn ensemble model of gradient descent boosting and BiPLS is proposed.With BiPLS as the base model method can reduce the sensitivity of boosting to noise.The gradient descent boosting strategy can improve the performance of base models.The iteration parameter and the segmentation parameter are fused to simplify the model.The final ensemble model can remain stable at different initial number of iterations.

Graphical abstractDisplay Omitted HighlightsBuild a better searching mechanism for ABC algorithm.Integrate information of global and previous best solutions into search strategy.Introduce two adaptive scaling factors for a better balance between exploration and exploitation. Artificial Bee Colony (ABC) algorithm is a wildly used optimization algorithm. However, ABC is excellent in exploration but poor in exploitation. To improve the convergence performance of ABC and establish a better searching mechanism for the global optimum, an improved ABC algorithm is proposed in this paper. Firstly, the proposed algorithm integrates the information of previous best solution into the search equation for employed bees and global best solution into the update equation for onlooker bees to improve the exploitation. Secondly, for a better balance between the exploration and exploitation of search, an S-type adaptive scaling factors are introduced in employed bees' search equation. Furthermore, the searching policy of scout bees is modified. The scout bees need update food source in each cycle in order to increase diversity and stochasticity of the bees and mitigate stagnation problem. Finally, the improved algorithms is compared with other two improved ABCs and three recent algorithms on a set of classical benchmark functions. The experimental results show that the our proposed algorithm is effective and robust and outperform than other algorithms.

The arrival of the current economic era has greatly stimulated the sound and rapid development of China’s overall national strength and has promoted the constant progress of all walks of life. With the acceleration of urbanization, the construction industry in China is constantly improving its technical level and expanding the industry scale. The main factors and countermeasures for construction project management are to improve the importance of the entire construction project. In this process, it is necessary to strictly supervise the relevant national departments to ensure their quality, and combine them with the management factors and countermeasures to improve. In view of the main factors and Countermeasures of construction engineering management, the construction quality and supervision are effectively controlled, and the quality of the construction is supervised and managed to ensure the safety and effective management of the whole construction quality. At the same time, it is necessary to continuously improve the countermeasures in this process, so that future construction projects can be more effective.

Liangliang Chen,* Junjun Wei,* Shuwei Zhang, Zhongguan Lou, Xue Wang, Yu Ren, Honggang Qi, Zhenhua Xie, Yirun Chen, Feng Chen, Qihang Wu, Xiaoxiao Fan, Honglei Xu, Shuaishuai Huang, Guobin Weng Department of Urologic Surgery, Ningbo Urology and Nephrology Hospital, Urology and Nephrology Institute of Ningbo University, Ningbo, China *These authors contributed equally to this work Background: The vitamin D receptor (VDR) plays a key role in vitamin-mediated signaling pathway. Emerging evidence has suggested that the VDR polymorphism may contribute to the risk of prostate cancer (PCa). However, the existing results are not conclusive in Asian population. Methods: We aim to evaluate the potential role of VDR polymorphisms on PCa of Asian population. PubMed, Scopus, Embase, Web of Science, Chinese National Knowledge Infrastructure, Wang Fang Data, and VIP Periodical were retrieved, and eligible studies (case–control or cohort study) meeting the inclusion criteria were evaluated through an updated meta-analysis using Stata13.0 software. Results: A total of 1,363 cases and 2,101 controls obtained from 13 eligible publications were eventually included in this meta-analysis. Our results show that a significant association of VDR taq1 polymorphism with PCa risk, especially in the Japanese population. In the clinical stage-stratified analysis, the pooled results revealed no significant difference in genetic polymorphisms between the local stage and control groups, whereas there was increased frequency of T allele and TT genotype in the advanced tumor stage group compared with local tumor stage or control groups. Similarly, no significant difference was seen in Gleason

This paper is concerned with the problem of improving undergraduate mathematical modelling competence in the current big data world. Firstly, we review the renowned teacher's seven-step, student's four-step and a six-step modelling cycles to explain mathematical modelling competence. The six-step modelling cycle is also used in Common Core State Standards for Mathematics (CCSSM) in the United States. Based on these previous modelling cycles, we propose a new five-phase modelling cycle by use of the tool named IBM SPSS Modeler in accordance with Cross Industry Standard Process for Data Mining (CRISP-DM). A teaching case will be presented to apply these methodologies to undergraduates’ mathematical modelling lessons. It is shown that teachers can use IBM SPSS Modeler to improve student's mathematical modelling competence by understanding and practicing the process of five-phase modelling. © 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:603–609, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21632

Endothelium has long been considered both a source and a target of systemic inflammation. However, to what extent endothelial activation contributes to systemic inflammation remains unclear. This study addresses the relative contribution of endothelial activation to systemic inflammation and multiple organ dysfunction and injury (MOD/I) in an E. coli peritonitis model of sepsis. We prevented endothelial activation using transgenic (TG) mice that conditionally overexpress a mutant I-kappaBalpha, a NF-kappaB inhibitor, selectively on endothelium. TG mice and their transgene negative littermates (WT) were injected with saline or E. coli (10(8) CFU per mouse). At 7 h after E. coli infection, markers of systemic inflammation, endothelial activation, and MOD/I were assessed. WT-E. coli mice showed significantly increased serum levels of TNF-alpha, IL-1beta, IFN-gamma, IL-6, KC, and MCP-1; tissue levels of TNF-alpha, IL-6, KC, MCP-1, ICAM-1, and VCAM-1; endothelial leakage index in heart, lungs, liver, and kidney; significantly increased serum levels of AST, ALT, BUN, and creatinine; and increased mortality. Blockade of NF-kappaB-mediated endothelial activation in TG mice had no effects on serum levels of TNF-alpha, IL-1beta, IFN-gamma, IL-6, KC, and MCP-1 (markers of systemic inflammation), and tissue levels of TNF-alpha, IL-6, KC, and MCP-1, but significantly reduced tissue levels of ICAM-1 and VCAM-1 (markers of endothelial inflammation and activation) in those four organs. TG-E. coli mice displayed reversed endothelial leakage index; reduced serum levels of AST, ALT, BUN, and creatinine; and improved survival. Our data demonstrate that endothelial NF-kappaB-driven inflammatory response contributes minimally to systemic inflammation, but plays a pivotal role in septic MOD/I, suggesting that endothelium is mainly a target rather than a source of systemic inflammation.

Archie formula, which contains three fundamental parameters (a, m, n), is the basic equation to compute the water saturation in a clean or shaly formation. These parameters are known as Archie parameters. To identify accurately the water saturation for a given reservoir condition, it depends critically on the accurate estimates of the values of Archie parameters (a, m, n). These parameters are interdependent and hence it is difficult to identify them accurately. So we present a new hybrid global optimization technique, where a gradient-based method with BFGS update is combined with an intelligent algorithm called Artificial Bee Colony. This new hybrid global optimization technique has both the fast convergence of gradient descent algorithm and the global convergence of swarm algorithm. It is used to identify Archie parameters in carbonate reservoirs. The results obtained are highly satisfactory. To further test the effectiveness of the new hybrid global optimization method, it is applied to ten non-convex benchmark problems. The outcomes are encouraging.

Controlling real-time distances between vehicles is extremely significant for keeping safe driving on highway. At the same time, status of traffic stream, whole time delay and errors in controlling also have impacts on the control at a high driving speed. In this paper, a self-optimizing PID model based on fuzzy control is proposed for assisting a car to follow another one at a safe distance. The proposed model is established on a wireless sensor network sharing information for perceiving different status of traffic stream. In the designed experiment, the fuzzy PID controller is able to adapt nonlinear and time-varying features of behaviors in traffic stream. Comparing with general PID controllers, the fuzzy PID controller has a better performance on dynamic behaviors. According to comparison with the effects of controlling in different conditions, simulation results show that the proposed model is able to adjust and control the follower's behaviors in different situations on highway.

The engineering optimization approaches arising in nonferrous metallurgical processes are developed to deal with the challenges in current nonferrous metallurgical industry including resource shortage, energy crisis and environmental pollution. The great difficulties in engineering optimization for nonferrous metallurgical process operation lie in variety of mineral resources, complexity of reactions, strong coupling and measurement disadvantages. Some engineering optimization approaches are discussed, including operational-pattern optimization, satisfactory optimization with soft constraints adjustment and multi-objective intelligent satisfactory optimization. As an engineering optimization case, an intelligent sequential operating method for a practical Imperial Smelting Process is illustrated. Considering the complex operating optimization for the Imperial Smelting Process, with the operating stability concerned, an intelligent sequential operating strategy is proposed on the basis of genetic programming (GP) adaptively designed, implemented as a multi-step state transferring procedure. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution with compact individuals. The optimal solution gained by evolution is a sequential operating program of process control, which not only ensures the tendency to optimization but also avoids violent variation by operating the parameters in ordered sequences. Industrial application data are given as verifications.

Color is related closely to human beings. Human life and habitat are full of colors. Color reflects human civilization and spiritual demands. It takes on the responsibility of embellishing life and beautifying environment. Karl Marx once said, the feel for color is the most popular form of general esthetics. With the rapid development of landscape architecture in our country, landscape architecture design has a higher and higher demand of color combination design, which means color planning in landscape design has become a typical issue.

The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE) algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS), the satisfactory solution is obtained by using a decision-making method for multiple attributes.

The endogenous CO 2 emissions are mainly from construction equipments powered with fossil fuel combustion at highway tunnel building period. These emissions from construction fossil fuel including gas, diesel, oil, coal and asphalt, are modeled and calculated to assess the effectiveness of highway construction. This paper selects a highway in western China as a case study to focus on the endogenous CO 2 emission during tunnel construction. It summarizes the results of field research that used a scientific literature. The formulary of counting methodologies of CO 2 emission considering the endogenous components is built up for highway tunnel construction. The oriented-diesel CO 2 emissions are at high level with the proportion of over 90% in comparison with other fuel types in tunnel building. The average endogenous CO 2 emission of tunnel construction is with the default value of 2911.5 kg/m. The research that produced these results should be continued to observe more highway tunnels of the same region in order to refine the current data and also gather data from other highways.

The interactive projection systems have been widely used in people's life. Currently the major type is based on interactive whiteboard (IWB). In recent years, a new type based on CCD/CMOS sensor is greatly developed. Compared to IWB, CCD/CMOS implements non-contact sensing, which can use any surface as the projection screen. This makes them more flexible in many applications. However, the main defect is that the location accuracy and tracing speed are limited by the resolution and frame rate of the CCD/CMOS. In this paper, we introduced our recent progress on constructing a new type of non-contact interactive projection system by using a two-dimensional position sensitive detector (PSD). The PSD is an analog optoelectronic position sensor utilizing photodiode surface resistance, which provides continuous position measuring and features high position resolution (better than 1.5μm) and high speed response (less than 1μs). By using the PSD, both high positioning resolution and high tracing speed can be easily achieved. A specially designed pen equipped with infrared LEDs is used as a cooperative target. A high precision signal processing system is designed and optimized. The nonlinearity of the PSD as well as the aberration of the camera lens is carefully measured and calibrated. Several anti-interference methods and algorithms are studied. Experimental results show that the positioning error is about 2mm over a 1200mm×1000mm projection screen, and the sampling rate is at least 100Hz.

In this paper, we propose a novel model for unsupervised Chinese event extraction. We use a multi-information fusion technique to combine two kinds of information for knowledge representation of event instances: language features and structure information. Then, we perform our proposed XLS-means Clustering Algorithm to group the candidate event instances into a "natural" number of clusters, which can fully take into account the similarity of both their language and structure information. The experimental results on ACE2005 Chinese corpus show that our model can achieve better performance than other unsupervised methods.

Relation Extraction is an important research field in Information Extraction. In this paper, we present a novel mixed model to extract relation between named entities in Chinese, which combines the merits of both feature based method and tree kernel based method. Feature based method captures the language information of the text, while, the tree kernel based method shows the structured information of the text. We evaluate the proposed model on the ACE(Automatic Content Extraction) 2005 corpus. The experiments show that our model can identify the majority of the non-relational instances and also has a good precision and recall rate on the identification of various relation types.

Triptolide is one of the major active components of traditional Chinese Medicine (TCM) - Tripterygium wilfordii Hook. f. (TWHf), which has been reported to be effective in the treatment of patients with a variety of inflammatory and autoimmune diseases, especially rheumatoid arthritis. However, its clinical use is restricted due to its scarce water solubility and some toxic effects. In order to find innovative ways for administering triptolide and alleviating its disadvantages, the novel types of delivery systems have been developed. This paper reviews the studies of triptolide- loaded nano drug delivery systems (NDDS) in our group during the past three years. The preparation, characterization, pharmacology and toxicity of triptolide-loaded solid lipid nanoparticles, microemulsions and polymeric nanoparticles were investigated. The results indicated that the NDDS presented more powerful activity and a lower toxicity in comparison with common drug carrier.

The 5th International Conference on Optimization and Control with Applications (OCA5) was held at the China University of Petroleum, Beijing, China on December 4–8, 2012. As a continuation of the OCA series, OCA5 provided an international forum for scientists, engineers, researchers and practitioners to exchange ideas and approaches, to present research findings and state-of-the-art solutions, to share experiences on potentials and limits and to open new avenues of research and developments on all issues and topics related to optimization and control. More than 200 representatives from over 20 countries and regions such as China, United States, Canada, Australia, Russia and Japan presented their latest work and discussed new progresses on optimization and control with applications. Conference participants were then invited to submit their expanded papers to Optimization Letters (OPTL). After peer reviews of all the submitted papers in accordance with the usual stringent reviewing process of OPTL, ten (10) papers have been accepted for publication in this special issue. The topics covered in this special issue include: derivative variation approaches for optimal control, primal-dual interior point methods for optimal design, penalty methods for finite-dimensional obstacle problem, modified

Pulmonary alveolar proteinosis (PAP) is a rare condition which is characterised by the abnormal accumulation of proteinaceous material in the alveolar spaces, with resulting impairment in oxygen exchange across the involved alveoli. The diagnosis of PAP can be established by the classic ‘milky’ effluent bronchoalveolar lavage fluid (BALF). The current effective treatment for PAP is whole-lung lavage (WLL). We offer one case of PAP with apparent presentations and the clinical course. This report provides some information about the diagnosis and treatment of PAP (see PAP video online). A 39-year-old man, an ex-smoker, was brought to our respiratory department due to dry cough and progressive exertional dyspnoea for 7 months. He had …