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This study aimed to provide evidence to support the use of a wiki called PBworks as a learning tool to foster students' information literacy (IL) skills based on activity theory.

The participants consisted of 421 students (i.e. form 1 to form 3) from Hong Kong taking a liberal studies course during the 2016–2017 academic year. This study mainly used a mixed methods design, proposing 11 hypotheses. Quantitative data from 374 questionnaires were analysed to test these research hypotheses, while a qualitative method (interviews) was used to explain the quantitative results. A structural equation modelling approach was used to analyse the data, and data triangulation was used to answer the same research questions.

The results showed that the model components PBworks affordances (PB) and rules and divisions (RD) had significant direct effects on individual activities (IA) and community activities (CA) and significant indirect effects on information literacy (IL). The results also revealed that CA had a significant effect on IA and had an even greater effect on IL.

Using PBworks and the project-based learning (PjBL) approach, this study examined the determinants affecting the IL skills of Hong Kong junior secondary school students and proposed a wiki-based information literary activity (WILA) model.

As students' IL skills have become increasingly important, this study can shed light on related topics for future studies.

And contribute to social stability and harmonious development.

This study eventually confirmed the validity of the WILA model with all hypotheses supported.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2020-0092.

This study aims to evaluate the protection performance of zinc as sacrificial anode for ABS A steel in the presence of H₂S under different temperatures, pH and salinities.

In this paper, weight loss measurements and electrochemical measurements are used to evaluate the corrosion degree of zinc and ABS A steel.

Under the conditions involved in this work, it is shown that zinc is a nice sacrificial anode with the reason of its stable potential and excellent anode current efficiency according to the relevant standard. And it is also found that the hydrogen evolution does not occur on ABS A steel specimens. The potential difference between cathode and anode is suitable; thus, it can be concluded that each steel is well protected.

To the best of the authors’ knowledge, no other study has analyzed the protection mechanism and effect of zinc as sacrificial anode in H₂S-containing environments under high temperature at present.

The purpose of this paper is to improve the accuracy of short time load forecasting to ensure the economical and safe operation of power systems. The traditional neural network applied in time series like load forecasting, easily plunges into local optimum and has a complicated learning process, leading to relatively slow calculating speed. On the basis of existing literature, the authors carried out studies in an effort to optimize a new recurrent neural network by wavelet analysis to solve the previous problems.

The main technique the authors applied is referred to as echo state network (ESN). Detailed information has been acquired by the authors using wavelet analysis. After obtaining more information from original time series, different reservoirs can be built for each subsequence. The proposed method is tested by using hourly electricity load data from a southern city in China. In addition, some traditional methods are also applied for the same task, as contrast.

The experiment has led the authors to believe that the optimized model is encouraging and performs better. Compared with standard ESN, BP network and SVM, the experimental results indicate that WS‐ESN improves the prediction accuracy and has less computing consumption.

The paper develops a new method for short time load forecasting. Wavelet decomposition is employed to pre‐process the original load data. The approximate part associated with low frequencies and several detailed parts associated with high frequencies components give expression to different information from original data. According to this, suitable ESN is chosen for each sub‐sequence, respectively. Therefore, the model combining the advantages of both ESN and wavelet analysis improves the result for short time load forecasting, and can be applied to other time series problem.