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The aim of this paper is to develop a new method to predict the potential shrinkage of plain‐knitted fabric.

The presented method is based on deformable curve. The delivered plain‐knitted fabric is represented as a deformable parametric curve, and the relaxed fabric can be reached by minimizing the energy of the curve. Compared to the delivered‐knitted fabric, the length and width shrinkage percentages can be calculated accordingly.

The new method is more convenient than the traditional trial and error method, and need less‐input parameters than the STARFISH technique. Experimental results show that this method is feasible.

This paper presents a new method for shrinkage prediction of plain‐knitted fabric based on deformable curve and energy minimum. The work can be linked with shrinkage control in textile industry.

This chapter uses an application to explore the utility of Bayesian quantile regression (BQR) methods in producing density nowcasts. Our quantile regression modeling strategy is designed to reflect important nowcasting features, namely the use of mixed-frequency data, the ragged-edge, and large numbers of indicators (big data). An unrestricted mixed data sampling strategy within a BQR is used to accommodate a large mixed-frequency data set when nowcasting; the authors consider various shrinkage priors to avoid parameter proliferation. In an application to euro area GDP growth, using over 100 mixed-frequency indicators, the authors find that the quantile regression approach produces accurate density nowcasts including over recessionary periods when global-local shrinkage priors are used.

The purpose of this paper is to propose a framework of the potential benefits of asset visibility in the context of returnable transport items (RTI), and uses the framework to examine the effect of asset visibility on the management of RTI systems.

A combined case study and simulation approach was used. A case study was performed to identify and understand how an existing RTI system is managed, while discrete‐event simulation was the method chosen to explore the potential effect of asset visibility.

The paper identifies cost aspects of implementing and operating RTI systems which may be influenced by asset visibility. The study implies that significant cost savings can be achieved through increased asset visibility, and highlights the importance of shrinkage and its impact on the operating cost of an RTI system. However, asset visibility alone is not enough; it requires proper actions and continuous management attention in order to attain the savings.

The results are derived from a single, combined case and simulation study.

The combined methods proved to be an efficient way of assessing and quantifying the potential effect of asset visibility along with the associated uncertainty in the results.

The paper provides an improved understanding of the effect of asset visibility on the management of RTI systems and complements existing visibility literature.

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

Electricity demand forecasting has always been a key issue, and inaccurate forecasts may mislead policymakers. To accurately predict China’s electricity demand up to 2030, this paper aims to establish a cross-validation-based linear model selection system, which can consider many factors to avoid missing useful information and select the best model according to estimated out-of-sample forecast performances.

With the nine identified influencing factors of electricity demand, this system first determines the parameters in four alternative fitting procedures, where for each procedure a lot of cross-validation is performed and the most frequently selected value is determined. Then, through comparing the out-of-sample performances of the traditional multiple linear regression and the four selected alternative fitting procedures, the best model is selected in view of forecast accuracy and stability and used for forecasting under four scenarios. Besides the baseline scenario, this paper investigates lower and higher economic growth and higher consumption share.

The results show the following: China will consume 7,120.49 TWh, 9,080.38 TWh and 11,649.73 TWh of electricity in 2020, 2025 and 2030, respectively; there is hardly any possibility of decoupling between economic development level and electricity demand; and shifting China from an investment-driven economy to a consumption-driven economy is greatly beneficial to save electricity.

Following insights are obtained: reasonable infrastructure construction plans should be made for increasing electricity demand; increasing electricity demand further challenges China’s greenhouse gas reduction target; and the fact of increasing electricity demand should be taken into account for China’s prompting electrification policies.

To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China’s logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights.

Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established.

Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods.

SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

The purpose of this paper is to describe the main ways in which large amounts of information have been integrated to provide new measures of food consumption and agricultural production, and new methods for gathering and analyzing internet-based data.

This study reviews some of the recent developments and applications of big data, which is becoming increasingly popular in agricultural economics research. In particular, this study focuses on applications of new types of data such as text and graphics in consumers' online reviews emerging from e-commerce transactions and Normalized Difference Vegetation Index (NDVI) data as well as other producer data that are gaining popularity in precision agriculture. This study then reviews data gathering techniques such as web scraping and data analytics tools such as textual analysis and machine learning.

This study provides a comprehensive review of applications of big data in agricultural economics and discusses some potential future uses of big data.

This study documents some new types of data that are being utilized in agricultural economics, sources and methods to gather and store such data, existing applications of these new types of data and techniques to analyze these new data.

This study aims to apply a Lean Six Sigma framework to support continuous improvement in the maritime industry (shipbuilding, logistics services and shipping companies) during COVID-19 pandemics. By applying the concepts of Lean Six Sigma and supply chain resilience, the most suitable continuous improvement method for the maritime industry is developed to maintain a resilient supply chain during COVID-19.

A specific shipbuilding, logistics services and shipping company in Indonesia is chosen as the research object. The Lean Six Sigma framework reveals the wastes through the supply chain resilience concept, and implements internal business processes to maintain optimal system performance.

The paper identifies important implementation aspects in applying Lean Six Sigma to shipbuilding, logistics services and shipping. The DMAIC (define, measure, analyze, improve and control) approach is applied to achieve supply chain resilience. Resilient measures are generated for the case companies to maximize performance during the pandemics.

This paper provides a new insight for integrating Lean Six Sigma and resilience strategies in the maritime industry during COVID-19 disruptions. The authors provide some insights to sustain the performance of the maritime industries under study.

This study is part of the first research in the maritime industry that focuses on continuous improvement during COVID-19 using Lean Six Sigma and supply chain resilience.

A single-domain multi-phase model is developed for macrosegregation and shrinkage pipe formation in castings, as functions of buoyancy- and shrinkage-induced flow. The paper aims to discuss these issues.

Using a volume of fluid (VOF) method, both the air/liquid and air/solid interfaces are tracked during shrinkage pipe formation. A set of mixture advection-diffusion equations are derived and solved for velocity, temperature, composition, and phase field evolution. The fluid mechanics of the model are verified using a transient ditch drainage problem.

Results showing the interaction of macrosegregation and pipe formation are presented for two alloys under faster and slower cooling conditions.

This model provides a comprehensive tool to investigate relationships between the developing composition distribution and shrinkage pipe formation.