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Studies on the construction defects and quality deviations have been commissioned and published in many countries, showing the global status of the issues related with construction defects. Therefore, the quality of the adapted practices in construction projects can be improved if the pattern of the sub-task, specifically the quality deviations and construction defects from the requirements and specifications is identified and understood. The purpose of this paper is to improve understanding of the behavior and pattern of the more sensitivity sub-task requirements (STRs) through the anatomy process of a particular task.

Six criteria have been classified for the levels of the estimated STRs quality deviations. The proposed study classifications were examined through the 3,030 cases of 17 STRs from compression concrete members (i.e. column).

Most of the STRs were found to be prone to deviations and the sensitivities of the STRs toward deviations and defects are varied across all STRs. The study reveals that three of the 17 STRs were observed as perfect works, ten STRs as acceptable works and four STRs as defective works.

The study found that the sub-task deviation patterns cannot be generalized and must be individually studied to recommend the appropriate proactive measures.

The purpose of this paper is to construct an optimal resource reallocation model of the limited resource by a moderator for reaching the greatest consensus, and show how to reallocate the limited resources by using optimization methodology once the consensus opinion is reached. Moreover, this paper also devotes to theoretically exploring when or what is the condition that the group decision-making (GDM) system is stable; and when new opinions enter into the GDM, how the level of consensus changes.

By minimizing the differences between the individuals’ opinions and the collective consensus opinion, this paper constructs a consensus optimization model and shows that the objective weights of the individuals are actually the optimal solution to this model.

If all individual deviations of the decision makers (DMs) from the consensus balance each other out, the information entropy theorem shows this GDM is most stable, and economically each individual DM gets the same optimal unit of compensation. Once the consensus opinion is determined and each individual opinion of the DMs is under an acceptable consensus level, the consensus is still acceptable even if additional DMs are added, and the moderator’s cost is still no more than a fixed upper limitation.

The optimization model based on acceptable consensus is constructed in this paper, and its economic significance, including the theoretical and practical significance, is emphatically analyzed: it is shown that the weight information of the optimization model carries important economic significance. Besides, some properties of the proposed model are discussed by analyzing its particular solutions: the stability of the consensus system is explored by introducing information entropy theory and variance distribution; in addition, the effect of adding new DMs on the stability of the acceptable consensus system is discussed by analyzing the convergence of consensus level: it is also built up the condition that once the consensus opinion is determined, the consensus degree will not decrease even when additional DMs are added to the GDM.

The purpose of this paper is to develop a dynamic control method to improve bus schedule adherence under connected bus system.

The authors developed a dynamic programming model that optimally schedules the bus operating speed at road sections and multiple signal timing plans at intersections to improve bus schedule adherence. First, the bus route was partitioned into three types of sections: stop, road and intersection. Then, transit agencies can control buses in real time based on all collected information; i.e. control bus operating speed on road sections and adjust the signal timing plans through signal controllers to improve the schedule adherence in connected bus environment. Finally, bus punctuality at the downstream stop and the saturation degree deviations of intersections were selected as the evaluation criteria in optimizing signal control plans and bus speeds jointly.

An illustrative case study by using a bus rapid transit line in Jinan city was performed to verify the proposed model. It revealed that based on the proposed strategy, the objective value could be reduced by 73.7%, which indicated that the punctuality was highly improved but not to incur excessive congestion for other vehicular traffic.

In this paper, the authors applied speed guidance and the adjustment of the signal control plans for multiple cycles in advance to improve the scheduled stability; furthermore, the proposed control strategy can reduce the effect on private traffics to the utmost extend.

This paper aims to propose a novel model predictive control (MPC) with time varying weights to develop a lateral control law in an automatic carrier landing system (ACLS), which minimizes landing risk and improves flight quality.

First, a nonlinear mathematic model of an F/A-18 aircraft during lateral landing is established. Then the landing model is linearized in the form of state deviations on the equilibrium points. Second, landing risk windows are proposed and a high-dimensional landing risk model is addressed through a back propagation (BP) neural network. The trained samples are acquired based on a pilot behavior model. Third, time varying weights created from the lateral landing risk are introduced into the performance function of MPC. Optimal solution is solved quicker and some state deviations are focused on and eliminated. Fourth, the algebraic inequalities are substituted by the linear matrix inequalities (LMIs), which are easily calculated by the computers.

On a semi-physical platform, the proposed method compares with a traditional MPC algorithm and a modified MPC with an additional term. The test results indicate that the proposed algorithm brings about an excellent landing performance as well as an ability of eliminating landing risk.

The landing phase of a carrier-based aircraft is one of the most dangerous and complicated stages, and the algorithm proposed by this paper plays a vital role in the lateral landing.

This paper establishes a lateral landing risk model, which considers not only the current landing state but also the future touchdown point. This lateral landing risk is integrated into the time varying weights of the MPC algorithm so that the state deviations and landing risk can be both reduced in the rolling optimization.

The purpose of this paper is to develop the multi-objective optimization approach and its numerical implementation to synthesise the model-base control for the part curing at autoclave processing, which supplies the stability and uniformity of the structure and mechanical properties of the material within the cured composite part.

The approach includes conversion of the cured part and mold geometry from their computer-aided design (CAD) to computer-aided engineering (CAE) representation, a finite element (FE) formulation of the coupled forward heat transfer/thermal kinetic problem with the parameters of prepreg, which should be determined by the thermal analysis, and, finally, a mapping of the area of 4D design space (thermal control parameters) to 2D objective space, whose coordinates are the maximum deviations of degree of cure and temperature within the cured part calculated at each call of the FE model.

The present modeling and optimization approach to the cure process control of the prepreg with thermosetting resin, as well as the means of visualizing optimization results, allow providing insight into complex curing phenomena, estimating the best achievable quality indicators of manufactured composite parts, finding satisfactory parameters of the control law and deciding considering all manufacturing constraints.

The research can be effectively used to optimize the cure process control for a wide class of polymeric composite parts, even with a complex geometry, but it requires the exact conversion of the geometry of the modeled part from the CAD to CAE environment, which implies the need for excluding all topological imperfections of original CAD model to eliminate the possible formation of void elements and other reasons that do not allow the correct FE meshing. Because thermal, rheological and kinetics parameters, which include the governing equations of cure process, depend on the reinforcing fibers, and especially on the resin properties, the thermal testing for the new modeled prepreg needs to be performed.

Computer implementation of the proposed approach and numerical method for model-based optimal control synthesis for composite part cure process can be used in aircraft, rotorcraft, ship and automotive technologies at the design of manufacturing process of the large composite parts with complex shape.

This will allow much better quality for large-scale composite parts, excluding very expensive, time-, energy- and material-consuming multiple cure process testing.

This is first time the problem of optimal control synthesis for curing the large-scale composite parts of complex shape was solved.

The first in first out (FIFO) rule is an indispensable rule in the automotive industry. However, during production, the order of coming to welding operation and exiting through the painting operation is broken. Consequently, the rule of FIFO also gets broken. The purpose of this paper is to eliminate the deviations of the automotive painting process in a just in time (JIT) environment.

The paper has been solved using observations and expert’s opinions and recorded data in the real problem area. Bypass technique and express line have been used as a real problem for the minimization of these deviations. The current process and the bypass process have been compared with simulation optimization.

This paper provides analytical insights about how to prevent delays during the JIT production painting process. Delays have been prevented by using the bypass technique and express line together. The number of delays reduced with an improvement of 129. The ratio of effect (for four months) has been founded to be 0.12. The output rate has been increased from 80% to 89%.

This paper focused on the number of vehicles in the cooling area of the painting process in the study; there may occur bottleneck processes or products. It does not contain station-based bottleneck processes.

From the real-case problem, one will see that the bypass technique and the express line can be applied and generalized to many similar problems and help companies conduct their assembly lines more efficiently.

This paper is the only example that has been used to bypass technique and express line together on the paint/cooling process in a JIT production system.

The purpose of this paper is to verify if adult education can contribute to social mobility by analysing how the socioeconomic and professional background of the students affects dropout and graduation hazards in higher education.

An event history analysis approach, with competing risks and discrete time, implemented under a multinomial logit model, is used to investigate how an extensive set of covariates affects the risk of graduation, dropout and persistence of 834 adult student workers from a higher education institution in Portugal.

Adult education may indeed be effective in promoting social mobility, as academic achievement is higher for student workers that have low educated parents and low income levels. Also, the probability of achieving graduation seems to be higher for those seeking for higher transformation.

Adult education should be encouraged as it generates both efficiency and equity benefits. Some policy recommendations are suggested for the higher education system to adapt better to the particular characteristics of adult workers and provide conditions to improve the job–study–family conciliation, namely, by adjusting the schedule and composition of classes, appreciating the curriculum and providing orientation to candidates, and introducing shorter/simplified versions of the degrees.

A separate treatment is given to adult student workers, whose characteristics are very particular, enriching the literature on academic achievement that has been focussed on traditional students. Additionally, the studied data set merges five sources and provides extensive and original information on personal, degree and employment variables of the students.

The purpose of this paper is to explore the effect of firm’s deviant strategy on analysts’ earnings forecasts and further examine the effects of firm’s information transparency and environmental uncertainty on these relationships from information asymmetry perspective.

The sample includes listed firms on Shanghai and Shenzhen Stock Exchange during the period 2007-2013.

The results indicate that firms’ deviant strategies have effects on analysts’ earnings forecasts, in particular, firms with extreme strategies have less analysts following, larger forecast error and dispersion compared with firms following industry norms. Moreover, information transparency and environmental uncertainty have effects on the relationship between strategic deviance and analysts’ earnings forecasts.

The empirical results of this paper provide strong evidence that strategy information is an important source of information for analysts’ earnings forecasts, which shows that analysts should pay attention to not only financial information but also the strategic information, especially when the information is related to strategic choice. In addition, it is necessary for investors to focus on strategic information to have a better understanding on financial information of enterprises and make better investment decisions.

The findings of this study indicate that corporate strategic deviance has an effect on analysts’ earnings forecasting behavior. This study enriches research studies on corporate strategy and external stakeholders and complements related research on analysts’ earnings forecasts from strategic perspective and information asymmetry perspective.

WeChat is the largest acquaintance social networking platform in China, in which users can view and reshare web pages shared by friends. This paper aims to analyze the spatio-temporal dynamics of web pages diffused in WeChat and advice on commercials.

A large number of web pages diffused in WeChat are collected and exclusively divided into four categories according to their titles, including advertisements, news bulletins, holiday greetings and emotional essays. For each web page, an information cascade (tree structure) is constructed to describe the diffusion trace. Based on the categories, the spatio-temporal popularity is characterized; the topological, temporal and spatial properties are examined; and the spatio-temporal diffusion velocity is explored.

Through comparative analysis, different categories of pages show diversity. For spatio-temporal popularity, there is no significant difference in cascade size; holiday greetings usually last for a relatively short time on average; emotional essays are more likely to spread to more provinces. For topological, temporal and spatial characteristics, the diffusion process of advertisements is more likely to be broadcasting than other categories; news bulletins and holiday greetings have an obvious bursty; the number of viewing behavior decreases from east to west in general. For spatio-temporal diffusion velocity, emotional essays diffuse the fastest in topological and spatio-temporal dimensions.

These findings contribute to promoting products and providing support for data driven modeling of information diffusion and human activity in spatio-temporal dimensions.

The accuracy and reliability of upper limb motion assessment have received great attention in the field of rehabilitation. Grasping test is widely carried out for motion assessment, which requires patients to grasp objects and move them to target place. The traditional assessments test the upper limb motion ability by therapists, which mainly relies on experience and lacks quantitative indicators. This paper aims to propose a deep learning method based on the vision system of our upper limb rehabilitation robot to recognize the motion trajectory of rehabilitation target objects automatically and quantitatively assess the upper limb motion in the grasping test.

To begin with, an SRF network is designed to recognize rehabilitation target objects grasped in assessment tests. Moreover, the upper limb motion trajectory is calculated through the motion of objects’ central positions. After that, a GAE network is designed to analyze the motion trajectory which reflects the motion of upper limb. Finally, based on the upper limb rehabilitation exoskeleton platform, the upper limb motion assessment tests are carried out to show the accuracy of both object recognition of SRF network and motion assessment of GAE network. The results including object recognition, trajectory calculation and deviation assessment are given with details.

The performance of the proposed networks is validated by experiments that are developed on the upper limb rehabilitation robot. It is implemented by recognizing rehabilitation target objects, calculating the motion trajectory and grading the upper limb motion performance. It illustrates that the networks, including both object recognition and trajectory evaluation, can grade the upper limb motion functionn accurately, where the accuracy is above 95.0% in different grasping tests.

A novel assessment method of upper limb motion is proposed and verified. According to the experimental results, the accuracy can be remarkably enhanced, and the stability of the results can be improved, which provide more quantitative indicators for further application of upper limb motion assessment.