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This paper aims to address problems in patient flow and identify the reasons behind extensive wait time at a public liver transplant outpatient clinic in an education and research hospital through the use of Lean health-care theories.

This paper opted for the application of Lean thinking and action research strategy. Data were collected through personal observations, interviews with users and team brainstorming. A value stream map was developed, improvement possibilities were identified and non-value-added activities were attempted to be eliminated.

Significant problems were identified and improvements were implemented and measured. The major remedial measures were: change the scheduling pattern, create a flow chart and a Kanban visual guide for medical students. In addition, an institutional change in the medical appointment scheduling software collaborated in the reduction of time and in the patient’s displacement. The waiting time was reduced by 4.5 h, and the per cent complete and accurate increased by 50 per cent.

The flow was redesigned, and a culture of continuous improvement was introduced. Visiting the place where work was being done, leaders identified and created more value to the process without significant costs. The Gemba Walk was a powerful tool, interacting with people and processes in a Kaizen spirit.

Public health services in developing countries are one of the most deprived social needs of good practice. It will be useful for those who need examples about how to apply Lean tools in health care.

The purpose of this paper is to investigate the effects of indigenous technological capability and interpersonal trust on product innovation through supply-chain learning under a dynamic environment.

The proposed model is tested with a sample of 300 manufacturers in China.

The results show that the learning from customers partially mediates the relationship between indigenous technological capability and product innovation, as well as between interpersonal trust with customers and product innovation. In addition, the influence of indigenous technological capability on learning from customers is weakened under a dynamic environment as well as the influence of learning from customers on product innovation.

This study illustrates the comparative roles of indigenous technological capability and interpersonal trust in learning customer knowledge and promoting innovation. It also enriches the innovation research by understanding the learning roles of indigenous technological capability and interpersonal trust under a dynamic environment.

The purpose of this paper is to solve difficult estimation problem on aircraft sudden fault by proposing a new pre-estimating method according to the energy evolution degree of the sensitive parameters to estimate the sudden fault. The sudden fault affects seriously the flight safety of aircraft.

It is based on the dissipative structure theory, and the evolution process of energy parameters is utilized. First, the evolution key points of sudden fault are determined by the time-varying entropy of sensitive parameters and the frequency band energy distribution. Then, we can obtain the evolution degree of sample while the evolution key points import the logistic regression (LR) model, and one can establish the pre-estimation model by means of relevance vector machine (RVM). While the evolution feature vector imports the RVM pre-estimation model, one can pre-estimate the sudden fault of aircraft.

The simulation results showed that this method can not only track the evolution process of aircraft sudden fault but also estimate its evolution degree, and it has a higher pre-estimating accuracy.

It provides a new way to forecast the sudden fault and increase the security of aircraft.

This paper proposes a pre-estimating method on aircraft sudden fault. It is based on the dissipative structure theory and the energy-sensitive parameters of the sudden faults are used. This method can enhance the security of aircraft and increase the protective ability of sudden fault on aircraft.

Trust is the glue that holds cooperative relationships together and often exists in an asymmetric manner. The purpose of this study is to explore how to mitigate the issue of losses or increased transaction costs caused by opportunistic behavior in a soft environment where trust asymmetry is quite common and difficult to avoid.

This study focuses on examining asymmetric trust between the government and the private sector in public-private partnership (PPP) projects. Drawing upon both project realities and relevant literature, the primary conditional variables influencing asymmetric trust are identified. These variables encompass power perception asymmetry, information asymmetry, interaction behavior, risk perception differences and government-side control. Subsequently, through the use of a survey questionnaire, binary-matched data from both the government and the private sector are collected. The study employs fuzzy-set qualitative comparative analysis (fsQCA) to conduct a configurational analysis, aiming to investigate the causal pathways that trigger asymmetric trust.

No single conditional variable is a necessary condition for the emergence of trust asymmetry. The pathways leading to a high degree of trust asymmetry can be categorized into two types: those dominated by power perception and those involving a combination of multiple factors. Differences in power perception play a crucial role in the occurrence of high trust asymmetry, yet the influence of other conditional variables in triggering trust asymmetry should not be overlooked.

The findings can contribute to advancing the study of trust relationships in the field of Chinese PPP projects. Furthermore, they hold practical value in facilitating the enhancement of trust relationships between the government and the private sector.

Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO₂ emission reduction of highways.

To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO₂ emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility.

The results show that through the application of the scheduling strategy to the HPWIS, the flexibility index of the Lianhuo Expressway increased by 29.17%, promoting a corresponding decrease in CO₂ emissions.

This paper proposed a new model to capture the evolution of the HESS, which provides highway transportation vehicles and service facilities with a clean electricity supply and achieves energy transfer aided by an energy storage system, thus forming a new model of a transportation energy system with source-grid-load-storage-charge synergy. An evaluation method is proposed to improve the air quality index through the coordination of new energy generation and environmental conditions, and dynamic configuration and dispatch are achieved with the master–slave game model.

The optimal payment in the whole operation and maintenance period of water environment treatment PPP projects has become the main approach to realize sustainable development of projects. This study is aimed at constructing an effective payment model for the whole life period of projects to achieve win-win among all stakeholders, so as to provide a theoretical reference and managerial implications for the public sector in the whole operation and maintenance period.

In the whole operation and maintenance period of water environment treatment PPP projects, this article investigates how the public sector optimizes the payment in the whole operation and maintenance period of projects. Firstly, the projects' whole operation and maintenance period is divided into several stages according to the performance appraisal period. And then, the multi-stage dynamic programming model is constructed to design the payment construct model for the public sector in each performance appraisal stage. The payment from the public sector is the decision variable, and the deduction from the private sector is a random variable.

The optimal payment model showed that the relatively less objective weight of public sector leaded to its relatively more total payment and vice versa. Therefore, the sustainable development of the projects can only be ensured when the objective weights both of them should be balanced. Additionally, the deduction from the performance appraisal of private sector plays an important role in the model construction. The larger deduction the private sector undertakes, the smaller profits private sector has. Since the deduction at each stage is a random variable, the deduction varies with the different probability distributions obeyed by the practical deduction in each stage.

The findings from this study have provided theoretical and application references, and some managerial implications are also given. First, the improvement of the pricing system of public sector should be accelerated. Second, the reasonable profit of the private sector must be guaranteed. While pursuing the maximization of social benefits, the public sector should make full use of the price sharing mechanism in the market and supervise the real income situation of the private sector. Third is increasing the public to participate in pricing. Additionally, it is a limitation that the deduction is assumed to conform to a uniform distribution in this study. Other probability distributions on deduction can be essentially further sought, so as to be more line with the actual situation of the projects.

The optimal payment in whole operation and maintenance period of the projects has become an important issue, which is a key to project success. This study constructs a multi-stage dynamic programming model to optimize payment in the whole period of projects. Additionally, this study adds its value through deeply developing the new theories of optimal payment to more suitable for the practical problems, so that to optimize the design of payment mechanism. Meanwhile, a valuable reference for public and private sectors is provided to ensure the sustainable development of the projects.

Unbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry.

The identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance.

The proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage.

The data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding; the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.

To achieve sustainability for a construction project requires the achievement of economic, environmental and social sustainability at the same time. The economic sustainability of a project should be built into the stipulated cost. The unbalanced bidding always contribute cost overrun for a construction project. The purpose of this paper is to develop a method for identifying unbalanced bidding.

The problem of identifying unbalanced bidding is a comprehensive evaluation problem. All line items and bidders in bills of quantities are evaluation factors and evaluation objects, respectively, and the engineer’s estimated prices are considered as the evaluation benchmarks. The weights of these factors are determined through integrating the entropy weight method and the analytic hierarchy model, which consider subjective and objective factors in the weighting process.

The proposed model can be used to quantitatively evaluate unbalanced bids. The identification process can be analyzed within a short period, specifically with the aid of Microsoft Excel, reducing delays in awarding contracts. The results show that the model will support owners as they efficiently and effectively identify unbalanced bids in the bid evaluation stage to mitigate the negative consequences of unbalanced bidding.

A case study is given to verify the feasibility and practicability of the proposed method, which not only assists owners in efficiently and effectively identifying unbalanced bidding, but also promotes the economic sustainability of construction projects.

The fuzzy comprehensive evaluation method is used to detect unbalanced bidding, allowing owners to be objective as they identify and reduce unbalanced bidding at the procurement stage.

The purpose of this paper is to provide an effective members-adding method for truss topology optimization in plastic design.

With the help of the distribution of principal stress trajectories, obtained by finite element analysis of the design domain, ineffective zones for force transmission paths can be found, namely, areas whose nodes may have ersatz nodal displacements. Members connected by these nodes are eliminated and the reduced ground structure is used for optimization. Adding members in short to long order and limiting the number of members properly with the most strained ones added, large-scale truss problems in one load case and multiple-load cases are optimized.

Inefficient members (i.e. bars that fulfil the adding criterion but make no contribution to the optimal structure) added to the ground structure in each iterative step are reduced. Fewer members are used for optimization than before; therefore, faster solution convergence and less computation time are achieved with the optimized result unchanged.

The proposed members-adding method in the paper can alleviate the phenomenon of ersatz nodal displacements, enhance computational efficiency and save calculating resources effectively.

Based on complex network theory, a method for critical elements identification of China Railway High-speed 2 (CRH2) train system is introduced in this paper.

First, two methods, reliability theory and complex theory, are introduced, and the advantages and disadvantages for their application in identifying critical elements of high-speed train system are summarized. Second, a multi-layer multi-granularity network model including virtual and actual nodes is proposed, and the corresponding fusion rules for the same nodes in different layers are given.

Finally, taking CRH2 train system as an example, the critical elements are identified by using complex network theory, which provides a reference for train operation and maintenance.

A method of identifying key elements of CRH2 train system based on integrated importance indices is introduced, which is a meaningful extension of the application of complex network theory to identify key components.