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We describe how wavelets may be used to solve partial differential equations. These problems are currently solved by techniques such as finite differences, finite elements and multigrid. The wavelet method, however, offers several advantages over traditional methods. Wavelets have the ability to represent functions at different levels of resolution, thereby providing a logical means of developing a hierarchy of solutions. Furthermore, compactly supported wavelets (such as those due to Daubechies) are localized in space, which means that the solution can be refined in regions of high gradient, e.g. stress concentrations, without having to regenerate the mesh for the entire problem. To demonstrate the wavelet technique, we consider Poisson's equation in two dimensions. By comparison with a simple finite difference solution to this problem with periodic boundary conditions we show how a wavelet technique may be efficiently developed. Dirichlet boundary conditions are then imposed, using the capacitance matrix method described by Proskurowski and Widlund and others. The convergence of the wavelet solutions are examined and they are found to compare extremely favourably to the finite difference solutions. Preliminary investigations also indicate that the wavelet technique is a strong contender to the finite element method.

The purpose of this paper is to examine how the characteristics of strategic performance measurement systems (SPMSs) influence the effectiveness of such systems. Specifically, the study examines the association between the following three strategic performance measurement approaches with the effectiveness of SPMSs: the use of multidimensional performance measures, the use of performance measures that are linked to value drivers, and the use of performance measures that are linked to strategy.

Data were collected using a mail questionnaire distributed to a random sample of 800 Australian manufacturing and service business units.

The use of multidimensional performance measures is found to positively influence the effectiveness of SPMSs.

Organisations need to strive to design their SPMSs in a manner which considers the achievement of both performance- and staff-related goals, with the findings suggesting that managers need to focus on a broad set of performance measures relating to the four dimensions of the BSC (financial, internal, customer, and learning and growth measures).

This study contributes to the literature by examining the important role that SPMSs play in the achievement of organisational process outcomes. The incorporation of a measure of organisational process effectiveness, and the subsequent identification of the performance-related outcome and staff-related outcome dimensions, provides future researchers with an alternative approach to analyse SPMS effectiveness and provides managers with an insight into how to adjust their SPMS to improve their organisational processes.

The purpose of this paper is to provide an update of the United Nations International Strategy for Disaster Reduction (UNISDR) campaign on Making Cities Resilient.

An opinion piece written by the Director a.i. of UNISDR and the Editors of the International Journal of Disaster Resilience in the Built Environment.

The campaign will continue and the focus will shift to more implementation support, city‐to‐city learning and cooperation, local action planning and monitoring of progress in cities.

Continued advocacy will seek to commit more cities and increase the support by national governments to support city resilience and local capacities.

The rapid development of e-commerce has brought not only great convenience to people but a great challenge to online stores. Phenomenon such as out of stock and slow sales has been common in recent years. These issues can be managed only when the occurrence of the sales volume is predicted in advance, and sufficient warnings can be executed in time. Thus, keeping in mind the importance of the sales prediction system, the purpose of this paper is to propose an effective sales prediction model and make digital marketing strategies with the machine learning model.

Based on the consumer purchasing behavior decision theory, we discuss the factors affecting product sales, including external factors, consumer perception, consumer potential purchase behavior and consumer traffic. Then we propose a sales prediction model, M-GNA-XGBOOST, using the time-series prediction that ensures the effective prediction of sales about each product in a short time on online stores based on the sales data in the previous term or month or year. The proposed M-GNA-XGBOOST model serves as an adaptive prediction model, for which the instant factors and the sales data of the previous period are the input, and the optimal computation is based on the proposed methodology. The adaptive prediction using the proposed model is developed based on the LSTM (Long Short-Term Memory), GAN (Generative Adversarial Networks) and XGBOOST (eXtreme Gradient Boosting). The model inherits the advantages among the algorithms with better accuracy and forecasts the sales of each product in the store with instant data characteristics for the first time.

The analysis using Jingdong dataset proves the effectiveness of the proposed prediction method. The effectiveness of the proposed method is enhanced and the accuracy that instant data as input is found to be better compared with the model that lagged data as input. The root means squared error and mean absolute error of the proposed model are found to be around 11.9 and 8.23. According to the sales prediction of each product, the resource can be arranged in advance, and the marketing strategy of product positioning, product display optimization, inventory management and product promotion is designed for online stores.

The paper proposes and implements a new model, M-GNA-XGBOOST, to predict sales of each product for online stores. Our work provides reference and enlightenment for the establishment of accurate sales-based digital marketing strategies for online stores.