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The purpose of this study is to extend the classical noncentral F-distribution under normal settings to noncentral closed skew F-distribution for dealing with independent samples from multivariate skew normal (SN) distributions.

Based on generalized Hotelling's T² statistics, confidence regions are constructed for the difference between location parameters in two independent multivariate SN distributions. Simulation studies show that the confidence regions based on the closed SN model outperform the classical multivariate normal model if the vectors of skewness parameters are not zero. A real data analysis is given for illustrating the effectiveness of our proposed methods.

This study’s approach is the first one in literature for the inferences in difference of location parameters under multivariate SN settings. Real data analysis shows the preference of this new approach than the classical method.

For the real data applications, the authors need to remove outliers first before applying this approach.

This study’s approach may apply many multivariate skewed data using SN fittings instead of classical normal fittings.

This paper is the research paper and the authors’ new approach has many applications for analyzing the multivariate skewed data.

Public-private partnership (PPP) projects for construction waste recycling have become the main approach to construction waste treatment in China. Risk sharing and income distribution of PPP projects play a vital role in achieving project success. This paper is aimed at building a practical and effective risk sharing and income distribution model to achieve win–win situation among different stakeholders, thereby providing a systematic framework for governments to promote construction waste recycling.

Stakeholders of construction waste recycling PPP projects were reclassified according to the stakeholder theory. Best-worst multi–criteria decision-making method and comprehensive fuzzy evaluation method (BWM–FCE) risk assessment model was constructed to optimize the risk assessment of core stakeholders in construction waste recycling PPP projects. Based on the proposed risk evaluation model for construction waste recycling PPP projects, the Shapley value income distribution model was modified in combination with capital investment, contribution and project participation to obtain a more equitable and reasonable income distribution system.

The income distribution model showed that PPP Project Companies gained more transaction benefits, which proved that PPP Project Companies played an important role in the actual operation of PPP projects. The policy change risk, investment and financing risk and income risk were the most important risks and key factors for project success. Therefore, it is of great significance to strengthen the management of PPP Project Companies, and in the process of PPP implementation, the government should focus on preventing the risk of policy changes, investment and financing risks and income risks.

The findings from this study have advanced the application methods of risk sharing and income distribution for PPP projects and further improved PPP project-related theories. It helps to promote and rationalize fairness in construction waste recycling PPP projects and to achieve mutual benefits and win–win situation in risk sharing. It has also provided a reference for resource management of construction waste and laid a solid foundation for long-term development of construction waste resources.

PPP mode is an effective tool for construction waste recycling. How to allocate risks and distribute benefits has become the most important issue of waste recycling PPP projects, and also the key to project success. The originality of this study resides in its provision of a holistic approach of risk allocation and benefit distribution on construction waste PPP projects in China as a developing country. Accordingly, this study adds its value by promoting resource development of construction waste, extending an innovative risk allocation and benefit distribution method in PPP projects, and providing a valuable reference for policymakers and private investors who are planning to invest in PPP projects in China.

This paper aims to understand the influence of cylinder liner temperature on friction power loss of piston skirts and the synergistic effect of cylinder liner temperature on lubrication and heat transfer between piston skirt and cylinder liner.

A method to calculate the influence of cylinder liner temperature on piston skirt lubrication is proposed. The lubrication is calculated by considering the different temperature distribution of the cylinder liner and corresponding piston temperature calculated by a new multilayer thermal resistance model. This model uses the inner surface temperature of the cylinder liner as the starting point, and the starting temperature corresponding to different positions of the piston is calculated using the time integral average. Besides, the transient heat transfer of mixed lubrication is taken into account. Six temperature distribution schemes of cylinder liner are designed.

Six temperature distributions of cylinder liner are designed, and the maximum friction loss is reduced by 34.4% compared with the original engine. The increase in temperature in the second part of the cylinder liner will lead to an increase in friction power loss. The increase of temperature in the third part of the cylinder liner will lead to a decrease in friction power loss. The influence of temperature change in the third part of the cylinder liner on friction power loss is greater than that in the second part.

The influence of different temperature distribution of cylinder liner on the lubrication and friction of piston skirt cylinder liner connection was simulated.

This paper investigates why bancassurance coexists with alternative insurance distribution channels in the long run, considering the bank channel is known to involve lower costs than traditional distribution systems. It tests the product-quality hypothesis that maintains that the higher costs of some distribution systems represent expenses associated with producing higher product quality, greater service intensity and/or skills to solve principal-agent conflicts.

An analysis is conducted on firms operating in the life segment of the Spanish insurance industry over an eight-year sample period. First, the author estimates cost efficiency and profit inefficiency using data envelopment analysis. Cost efficiency enables one to evaluate if the use of the banking channel increases cost efficiency. Profit inefficiency is addressed to identify the existence/absence of product-quality differences. The performance implications of using bancassurance are analyzed by applying Heckman's two-stage random-effects regression model.

The results support the product-quality arguments. The use of banking channel was found to increase cost efficiency. However, the distribution channel/s utilized did not affect profit inefficiency.

A regulatory environment that supports the development of bancassurance enables this and alternative distribution channels to be sorted into market niches, where each system enjoys comparative advantages in order to minimize insurer costs and maximize insurer revenues. There is no single optimal insurance distribution system.

This is the first study to investigate why bancassurance coexists with alternative insurance distribution channels.

Over the last few years, distribution developments in non‐food have been considerable. Centralisation and contract distribution have gained favour, and an increasing number of companies are re‐appraising their choice of distribution channels and systems. Just as in food, distribution systems are having to be changed to meet the demands of the consumer.

One of the basic problems hindering effective implementation of the physical distribution management (PDM) concept in many companies today is organisational rigidity. By this is meant the inability and unwillingness of management to respond to the changing demands of new concepts and new techniques inherent in the successful adoption of PDM in existing corporate organisations. This rigidity manifests itself in two related ways: firstly, in the relationships structure within an organisation, and the place of physical distribution relative to other functional areas in management, such as marketing, production, finance, etc., and secondly, in the characteristics of distribution executives and their perceived role within an organisation.

Physical distribution organisations may be defined as organisational units whose duty is to administer economic activities that impact upon the flow of finished goods between points of production and consumption. Physical distribution components occupy a unique role in the organisation. Their mission has been defined as “getting the right assortment of materials to the right location in an efficient manner timely to marketing and manufacturing requirements”. To accomplish this mission there must be continual interaction between suppliers of materials and receivers of materials. Material suppliers can be thought of as the rest of the organisation of which the physical distribution component is a part, primarily the production or manufacturing component. Receivers of materials are the organisation's customers or distribution points. The physical distribution organisation, by virtue of the activities it performs, must deal with both the internal suppliers and the external receivers.

The physical distribution function of a firm is a complex process. It consists of all the activities involved in the flow of goods from the raw material supplier to the final consumer and incorporates the major activity centres of purchasing, warehousing, transportation, order processing, and inventory control. The goal of a firm's distribution operation is to insure that established customer service levels are achieved at a minimum total cost.

This study aims to estimate the firm size distributions that belong to the service sector and manufacturing sector in Korea.

When estimating the firm size distribution, the author considers the following two major factors. First, the firm size distribution can have a gamma distribution rather than traditional accepted distributions such as Pareto distribution or log-normal distribution. In particular, industry-specific enterprises can have different size distributions of the type of gamma distribution. Second, the firm size distribution that is applied to this study’s data set should reflect a number of factors. For example, estimating mixture gamma distribution for firm size distribution should be required and compared, because the total amount of configuration data is composed of small businesses, medium-sized and large companies.

Using 8,230 number of firm data in 2013, the author estimates mixture gamma distribution for the firm size.

From the comparison, empirical results are found for the following characteristics of core firm size distribution: first, the firm size distribution of the manufacturing sector has a longer tail than firm size distribution of the service sector. Second, the manufacturing firm size distribution dominates the entire country firm size distribution. Third, one factor among the three factors that make up the mixed gamma firm size distribution is described for 99 per cent of the firm size distributions. From the estimated firm size distributions of the service sector and manufacturing sector in Korea, the author simply implies the strategy and policy implications for the start-up firm.

The concept of the evolution of the distribution function is used to derive an energy‐scale distribution that is able to describe transport phenomena, including inter‐valley transfer effect, in the scale as small as the energy relaxation time. The energy‐scale distribution is used to study the evolution of electrons in n‐type GaAs under the influence of rapid change in field. Results indicate that, near the peak of strong velocity overshoot or the bottom of pronounced undershoot in the Γ valley caused by the rapid change in field, the energy‐scale distribution can not respond as fast as the distribution function calculated from the Monte Carlo method. The average velocity resulting from the energy‐scale distribution therefore leads to less pronounced overshoot and undershoot than those obtained from the Monte Carlo method. However, since velocity overshoot and undershoot are not pronounced in the L‐valleys, the L‐valley energy‐scale distribution is in excellent agreement with that determined by the Monte carlo simulation.