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From the intra- and inter-regional dimensions, this paper investigates the linkage between industrial agglomeration and inventory performance, and further demonstrates the moderating role of firm size and enterprise status in the supply chain on this linkage.

Using a large panel dataset of Chinese manufacturers in the Yangtze River Delta for the period from 2008 to 2013, this study employs the method of spatial econometric analysis via a spatial Durbin model (SDM) to examine the effects of industrial agglomeration on inventory performance. Meanwhile, the moderation model is applied to examine the moderating role of two firm-level heterogeneity factors.

At its core, this research demonstrates that industrial agglomeration is associated with the positive change of inventory performance in the adjacent regions, whereas that in the host region as well as in general does not significantly increase. Additionally, both firm size and enterprise status in the supply chain can positively moderate these effects, except for the moderating role of firm size on the positive spillovers.

In view of firm heterogeneity, managers should take special care when matching their abilities of inventory management with the agglomeration effects. Firms with a high level of inventory management are suited to stay in an industrial cluster, while others would be better in the adjacent regions to enhance inventory performance.

This paper is the first to systematically analyze the effects of industrial agglomeration on inventory performance within and across clusters, and confirm that these effects are contingent upon firm size and enterprise status in the supply chain. It adds to the existing literature by highlighting the spatial spillovers from industrial clusters and enriching the antecedents of inventory leanness.

The purpose of this study is to identify and evaluate supply chain strategies (SCSs) that drive financial performance to guide practitioners, especially in liquefied natural gas (LNG) networks, to review and adopt SCSs that drive competitiveness and value creation for investors.

Analytical hierarchy process (AHP) was deployed to prioritise SCSs according to their relative impact on financial performance in LNG networks. Interviews with experts were analysed using template analysis to establish latent drivers of financial performance specific to LNG networks.

Results support the significant role of SCSs in improving financial performance. Although findings prioritised collaborative strategy as the most important driver of financial performance in LNG networks, to fully optimise financial outcomes, all the SCSs should be implemented across LNG networks as no single strategy in isolation is a standalone driver of financial performance.

The AHP model provides a novel ranking for SCSs and measures to guide decision-makers. LNG practitioners may exploit the results to make informed decisions.

The study extends previous literature by proposing a framework and a new LNG empirical model that facilitates understanding of how SCSs contribute positively to financial performance and support practitioners in making strategic supply chain decisions.

With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are gaining in popularity. However, the application of deep neural networks in the material science domain is mainly inhibited by data availability. In this paper, to overcome the difficulty of multifactor fatigue life prediction with small data sets,

A multiple neural network ensemble (MNNE) is used, and an MNNE with a general and flexible explicit function is developed to accurately quantify the complicated relationships hidden in multivariable data sets. Moreover, a variational autoencoder-based data generator is trained with small sample sets to expand the size of the training data set. A comparative study involving the proposed method and traditional models is performed. In addition, a filtering rule based on the R2 score is proposed and applied in the training process of the MNNE, and this approach has a beneficial effect on the prediction accuracy and generalization ability.

A comparative study involving the proposed method and traditional models is performed. The comparative experiment confirms that the use of hybrid data can improve the accuracy and generalization ability of the deep neural network and that the MNNE outperforms support vector machines, multilayer perceptron and deep neural network models based on the goodness of fit and robustness in the small sample case.

The experimental results imply that the proposed algorithm is a sophisticated and promising multivariate method for predicting the contact fatigue life of a coating when data availability is limited.

A data generated model based on variational autoencoder was used to make up lack of data. An MNNE method was proposed to apply in the small data case of fatigue life prediction.

This paper investigates how state governments used budget balancing strategies to cope with budget shortfalls in the fiscal years between 2009 and 2013. Using data from the Fiscal Survey reports and Comprehensive Annual Financial Statements (CAFRs) covering all fifty states, the paper summarizes and analyzes several types of strategies such as state savings, federal aid, revenue enhancement and expenditure cutting in response to budget shortfalls during and after the Great Recession of 2008. In addition, findings from the three case studies in New York, Texas and Washington show distinct patterns in these states’ choices of balancing strategies to cope with budget shortfalls. New York adopted a more balanced approach between revenue increasing and expenditure cutting strategies, whereas Washington and Texas implemented more severe expenditure cutting strategies to address budget shortfalls.

Special education in China has lagged behind regular education for many years, however, the past few decades, the government has made considerable efforts to develop and improve the special education system. While the citizens of China have had a generic moral interest in disability since ancient times, the development of special education schools did not occur until American and European missionaries started schools for the visually and hearing impaired in the 19th century. The next major influence in the development of the special education system occurred with China’s Cultural Revolution in 1978. Interestingly, there is not any exclusive legislation on special education but in the 1980s, the government started Learning in Regular Classrooms (LRC), which is China’s version of inclusion. LRC has progressed rapidly the past two decades; however, the quality of instruction is low due to a lack of specialists, a shortage of personnel, inadequate funding, and limited technology as well as other barriers that are delineated in the chapter. The chapter emphasizes the government’s recent efforts in in-service teacher training, the preparation of preservice teachers, working with families, developing community rehabilitation training programs, and implementing evidence-based practices. Special education in China today is at a good place but it has quite a way from the ideal situation.

The case focuses on the establishment and development of LYFEN, a Chinese leisure food brand. LYFEN created a business model of “small packaging + store.” Through accurate analysis of consumer habits, it quickly became one of the major brands in China's leisure food industry. In the process of entrepreneurship, it grasped the economic opportunity during SARS and quickly bought a large number of stores at low prices, laying the foundation for the rapid development of the follow-up. At the same time, its active practice of introducing information construction also further improved the business's competitiveness. Case B mainly focuses on the external and internal environment of LYFEN after 2015. According to estimates made in 2014, LYFEN's online sales were supposed to double, but LYFEN was gradually falling behind the competition.

This chapter presents several approaches for identifying and dating the speculative bubble on real estate market. Using the real estate price index (IPAI), statistical and structural approaches were combined in order to detect the existence of a bubble on the Moroccan real estate market. The results obtained affirm that the Moroccan real estate market experienced a speculative bubble during the period 2006–2008 explained mainly by the boom of credit during the same period. The use of the Markov switching model affirmed that the speculative bubble on Morocco is cyclic and consequently corroborates the critic formulated by Evans (1991) concerning the traditional approaches for the detection of financial bubbles. Thus, the analysis of the series of the bubble, extracted using the Kalman filter, affirms the existence of two regimes, namely an explosive regime and a normal regime. The first regime describes the periods of explosion of the bubble and lasts for about 9 quarters, while the second, lasting for 14 quarters, describes the periods of return to the average cycle.

A preceding paper by Baum examined the valuation of reversionary freehold interests, distinguishing between conventional and modern approaches. This paper applies the same approach to the valuation of leaseholds, and falls into two parts. Part 1 examined conventional leasehold valuations and the criticisms that may be made, concluding that both dual rate and single rate conventional valuations should be abandoned except in limited circumstances. Part 2 identifies three alternative modern approaches — real value, rational model and DCF — and compares their use in three general variations of leasehold valuation. The results are compared, and recommendations for their use are made. Finally an overview of the application of modern approaches to investment property valuation is presented.

The goal of this chapter is to respond to the theoretical inquiries by scholars who are interested in how the public–private partnership (PPP) models adapt to China’s context where political power dictates economic strategies. We also want to provide suggestions to policy designers who aim to promote a sustainable investment environment for domestic and international investors. We review the literature that explains the upside and downside of PPP projects in contemporary China. (1) We classify the trajectory of PPP evolution into four phases, i.e., emergence, growth, recession and revival. (2) We note that private companies take a disadvantageous position in the partnership compared with governments and state-owned enterprises because of a lack of specialized legislation, unequal competition between private companies and state-owned enterprises and the opposition from the civic society. (3) We identify political risks as the most influential risks. Political risks also lead to the misallocation of other risks between public and private parties that contributes to the high failure rate of China’s PPP projects. Based on these findings, we recommend governments to draft specialized legislation, stabilize the political environment and provide favourable subsidies to local governments to limit the risks involved in PPP projects. We also advise private enterprises and state-owned enterprises to focus on negotiating over task and risk division with governments when they make decisions to participate in PPP projects. This full review of studies on PPP development in China provides reliable recommendations to scholars, governments and enterprises.