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Background: Pain is a common complication after mixed hemorrhoids, which seriously affects the recovery of patients and prolongs the length of hospital stay. Acupoint catgut embedding has advantages in improving a variety of acute and chronic pain diseases, but there is still a lack of rigorous randomized controlled studies to verify its efficacy and safety in the treatment of postoperative pain of mixed hemorrhoids. Therefore, the purpose of this randomized controlled trial is to evaluate the clinical efficacy of acupoint catgut embedding in the treatment of postoperative pain of mixed hemorrhoids. Methods: This is a prospective randomized controlled trial to study the efficacy and safety of acupoint catgut embedding in the treatment of postoperative pain of mixed hemorrhoids. Approved by the clinical research ethics committee of our hospital, the patients were randomly divided into observation group and control group according to 1:1. The observation group received acupoint catgut embedding before the operation, while the control group received no special treatment. The efficacy and safety indexes were concerned after the operation, and the observation indexes included: resting state and VAS score during defecation, postoperative hospitalization time, total amount of analgesic use, adverse reactions, etc. Finally, we carried on the data statistical analysis through the SPSS version 19.0. Discussion: This study will evaluate the efficacy and safety of acupoint catgut embedding in the treatment of postoperative pain of mixed hemorrhoids, and the results of this study will provide a new idea for the selection of postoperative analgesia for mixed hemorrhoids resection.

This paper takes the risk management and countering strategies of a project as the basis of theoretical knowledge, IT modeling and simulation technology. By combining this approach with the processes of large international construction projects, we specifically research the political risk, natural risk, economic risk, design risk and risk of construction preparation in the project approval and early preparation stages. We also emphasize the risk identification, risk estimation, risk evaluation, risk response, risk monitoring and risk management decisions for the importance of the practice of large international projects and subsequently indicate the primary issues and countering strategies in international project risk management that should receive attention during a construction project.

In common condition we can say that the tower crane is overloaded when the value of lifting load is far more than that of design. As a matter of fact, because of the influences of wind load and loose bolts some collapsed accidents of tower crane took place while lifting load was less than design value. So in this paper a model named TLIM(tower crane loading inclination feature model) is established based on TCBIM(tower crane body inclination model). There are three differences between TLIM and normal healthy model: first, the coordinate of tower body top inclination is in the front of TCBIM when it is under the condition of severe overload; second, it is can be defined that the critical collapsed state is the suitable external envelope of tower body top inclination; at last, the angle difference δ θ between tower body top inclination and TCBIM is measurable. It is proved in this paper that the model TLIM is correct through analyzing experimental data, and not only is it significant to the auto-detect of tower collapsed accidents caused by overload, but also a feasible method for people to monitor and prevent tower from collapsing.

It is a major hidden danger of accidents in work when tower crane foundation is sinking and slope, it has been one of the difficulties in the field of tower crane's design and research that how to detect the feature of sinking and slope automatically and distinguish this feature from other hidden danger characteristics. In this paper, wind and inertia force are not considered and the model of TFSM (Tower Crane Foundation Slope Model Based On Inclination Feature) during working and non-working states is established based on the model of TCBIM(Tower Crane Body Inclination Model). The differences between TFSM and the model of healthy state are shown below: First, considering two conditions that the foundation is of healthy and sinking, the area of external envelope arrange equivalent inclination is more than the normal rectangular value considering foundation is of health. Second, when the tower crane is unload and rotates, the shape of external envelope arrange equivalent inclination is unchanged whether the foundation is sinking or not, but the positions of it will be different. Thrid the centroid of external envelope arrange equivalent inclination is deviated from the centroid of normal healthy model, and the distance is measurable and the distance value is δ θ in this paper, It is proved that the model is not only correct by analyzing data provided, but also very significant for the automatic detection of sinking of the foundation, at the same time, it is provided a simple and feasible method to detect and prevent tower crane from sloping.

The tower crane fault status diagnosis has been one of difficult problems in the field of hoisting and transport. How to determine whether the tower crane steel structure meet the design requirements, and whether tower crane work normally and so on have been the trouble for all tower crane owners and managers. In this paper we studied the mechanical model of the tower crane under normal state and then obtained three inclination characteristics on the inclination of tower body top: First, when the tower crane acted upon by different hoisting moment, there is external envelope in the range of inclination that is tower body top relative to the vertical line, Second, when the moment acting on tower body top is zero, the inclination that is tower body top relative to the vertical line is on the line which sees the centre point in the inclination range under the unloaded state and centre point in the inclination range under rated loaded state as its endpoints, Third, the external envelope of tower body top inclination is limited, having testability. Through measuring the normal working state data of four kinds of common tower cranes, this paper draws to prove the correctness of it. TCBIM can be used for a variety of fault conditions and diagnosing whether tower crane is in the normal state. It is worthwhile for the similar diagnosis of all tower steel structure.

Optimization design of tower crane steel structure is very important for design technique development of tower crane. This paper proposes a kind of parametric optimization model based on Ansys OPT, combined with finite element analysis (FEA) and overall stability theory. The tower body is taken as an example to introduce the building process and the program structure of the parametric optimization model. The combination of calibrating of overall stability and single-limb stability and FEA and OPT is realized through extracting some paramete from FEA results, such as weight and axial force, etc. The realization of the key technology is discussed. A rapid optimization design of tower body is realized by developing corresponding program using ansys parametric design language (APDL). It has a referenced meaning for other optimization design of steel structure.

Many video surveillance systems are demanding to monitor moving human behaviors, especially for such environments as jails, warehouses, supermarkets, secret rooms, offices, auditoriums and so on. It has universal significance for image capture of surveillance system to recognize moving human bodies, accordingly, it is an imperative to recognize moving human bodies from other moving objects rather than distinguish differences amongst human bodies. In this paper, the frame difference method is employed for image segmentation of moving objects, matching algorithms such as human body shapes, skin colors and postures are studied. By synthesizing information features of human body shape data and skin color data, a matching algorithm for moving human bodies is established. This algorithm is computationally efficient, and can be applied to resource-restricted systems e.g. embedded systems. The experimental results show that this algorithm can accurately segment moving human bodies from complex background, and can greatly reduce the calculation workload, in addition, it needs less storage spaces and has good robustness and accuracy. Therefore this algorithm can be pragmatically applied to video surveillance via automatically controlling lenses towards moving human bodies.

For the natural scene recognition and moving object segmentation, the segmentation of tree crown could improve the recognition rate and segmentation accuracy of moving objects when they are in strong wind and no buildings. Up to now, many researches mainly focus on the background modeling and texture synthesizing. In this paper, feature modeling of tree crown is discussed and modified by using the technique of remote sensing physics and texture synthesizing, and the feature model of tree crown is given based on the texture of tree crown. At the same time, the method and procedure for modeling and image processing are presented, and it is proved that the feature model is feasible according to the experimental results.

Nonlinear mixed effects models (NLMEs) are very popular in many longitudinal studies such as HIV viral dynamic studies, pharmacokinetics analyses, and studies of growth and decay. In these studies, however, missing data problems often arise, which make some statistical analyses complicated. In this thesis, we proposed an exact method and an approximate method for NLMEs with random-effects based informative dropouts and missing covariates, and propose methods for simultaneous inference. Monte Carlo E M algorithms are used in both methods. The approximate method, which is based on a Taylor series expansion, avoids sampling the random effects in the E-step and thus reduces the computation burden substantially. To illustrate the proposed methods, we analyze two real datasets. The exact method is applied to a dataset with covariates and a dataset without covariates. The approximate method is applied to the dataset without covariates. The result shows that, for both datasets, dropouts may be correlated with individual random effects. Ignoring the missingness or assuming ignorable missingness may lead to unreliable inferences. A simulation study is performed to evaluate the two proposed methods under various situations.