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The present study aims to develop a risk-supported case-based reasoning (RS-CBR) approach for water-related projects by incorporating various uncertainties and risks in the revision step.

The cases were extracted by studying 68 water-related projects. This research employs earned value management (EVM) factors to consider time and cost features and economic, natural, technical, and project risks to account for uncertainties and supervised learning models to estimate cost overrun. Time-series algorithms were also used to predict construction cost indexes (CCI) and model improvements in future forecasts. Outliers were deleted by the pre-processing process. Next, datasets were split into testing and training sets, and algorithms were implemented. The accuracy of different models was measured with the mean absolute percentage error (MAPE) and the normalized root mean square error (NRSME) criteria.

The findings show an improvement in the accuracy of predictions using datasets that consider uncertainties, and ensemble algorithms such as Random Forest and AdaBoost had higher accuracy. Also, among the single algorithms, the support vector regressor (SVR) with the sigmoid kernel outperformed the others.

This research is the first attempt to develop a case-based reasoning model based on various risks and uncertainties. The developed model has provided an approving overlap with machine learning models to predict cost overruns. The model has been implemented in collected water-related projects and results have been reported.

Nowadays, online platforms which provide products or services try to implement their homegrown communities to facilitate users' social interactions. Reviewers' activities in these communities can reflect their interests. Based on the theory of homophily, the authors aim to explore the impacts of the reviewer preference similarity and opinion similarity on the rate of product diffusion.

First, the authors construct reviewer similarity network based on their common interests and propose typical network metrics to measure reviewer preference similarity. Second, the authors measure reviewer opinion similarity with natural language processing. Finally, based on a panel data from an online video platform in China, both the fixed-effect and random-effect panel data models are constructed.

The authors find that reviewer preference similarity has a positive effect on the product diffusion, whereas reviewer opinion similarity has a negative effect on the diffusion. Furthermore, temporal distance moderates the relationship between reviewer similarity and the product diffusion. As a double-edged sword, review preference similarity hinders product diffusion in the initial phase, whereas benefits it in the later phase. Reviewer opinion similarity is always detrimental to product diffusion, especially in the initial phase.

This paper extends the understanding of homophily from the micro peer level to the group level by constructing reviewers' similarity network and highlights the important role of reviewer preference similarity and opinion similarity in product diffusion. The results also provide important insights for managers to design and implement diversity strategies for better product adoption in the community context.

In recent years, technology diffusion, globalization and the Internet revolution have accelerated the growth of online transactions and altered corporate operations systems. The emergence of computer technology and the Internet have changed the way businesses work. The purpose of this study is to find and identify any common patterns in the logistics and supply chain industries for job requirements using job posting content in Malaysia.

This study provides an exploratory assessment of the employability skill set required using online job posting advertisements. Online job posting advertising, also known as e-recruiting, is one field that has been significantly influenced by information technology. In addition, the current Covid-19 outbreak has created a new need for a long-term contactless talent acquisition process in the organization's operating systems.

Based on this study's findings, the top ten skills required by employers for logistics and supply chain positions are (1) supply chain analytics, (2) technological aptitude, (3) teamwork skills, (4) customer focus, (5) leadership skills, (6) interpersonal skills, (7) people skills, (8) creativity and resilience, (9) demand and supply forecasting ability, and (10) project management skills. Overall, the findings provide a road map for practitioners and academics interested in developing supply chain managers' necessary skills and competencies to manage current and future supply networks. It also allows companies to adjust their supply chain management hiring, training and retention methods.

Although the study was done in Malaysia, the supply chain skills and competencies stated in this study, as well as their categorization, can be applied in other developing countries.

This study aims to investigate the conditional indirect effect of academic self-efficacy in the interplay between academic motivation and academic satisfaction through academic engagement among university students.

A cross-sectional study was performed on 1,638 Vietnamese university students (31.9% males and 68.1% females) aged 16 to 36 (Mean = 20.06, SD = 1.428). The participants filled out a questionnaire with the Vietnam versions of the General Self-Efficacy Scale, Academic Motivation Scale, Academic Life Satisfaction Scale and Academic Engagement Scale. Model 4 and Model 7 in the PROCESS macro were used for the mediation analysis and the moderated mediation analysis.

Results showed that the indirect effect of academic engagement on the academic motivation-academic satisfaction link was significant. Furthermore, academic self-efficacy moderated this indirect effect. The indirect effect was stronger among students with high academic self-efficacy and weaker among students with low academic self-efficacy.

This study’s findings contribute to educational research on academic satisfaction and can be used by institutions of higher education and educators to enhance academic satisfaction among university students.

This study aims to explore the synergistic effect of oxide nanoparticles (ZnO, Fe₂O₃, SiO₂) and cerium nitrate inhibitor on anti-corrosion performance of epoxy coating. First, cerium nitrate inhibitors are absorbed on the surface of various oxide nanoparticles. Thereafter, epoxy nanocomposite coatings have been fabricated on carbon steel substrate using these oxide@Ce nanoparticles as both nano-fillers and nano-inhibitors.

To evaluate the impact of oxides@Ce nanoparticles on mechanical properties of epoxy coating, the abrasion resistance and impact resistance of epoxy coatings have been examined. To study the impact of oxides@Ce nanoparticles on anti-corrosion performance of epoxy coating for steel, the electrochemical impedance spectroscopy has been carried out in 3% NaCl solution.

ZnO@Ce3+ and SiO2@Ce3+ nanoparticles provide more enhancement in the epoxy pore network than modification of the epoxy/steel interface. Whereas, Fe2O3@Ce3+ nanoparticles have more to do with modification of the epoxy/steel interface than to change the epoxy pore network.

Incorporation of both oxide nanoparticles and inorganic inhibitor into the epoxy resin is a promising approach for enhancing the anti-corrosion performance of carbon steel.

The goal of this study is to identify and validate some selected determinants of early-stage entrepreneurial activity (ESEA) by assessing the impact of entrepreneurial knowledge and skills (EK&S), fear of failure (FoF), the social status of entrepreneurs (SSE) and entrepreneurial intentions (EI) on ESEA.

The study utilised cross-sectional data gathered by the Global Entrepreneurship Monitor (GEM) team from 49 countries, with a total of 162,077 respondents. The data analyses involved correlation, simple regression and path analyses, with a specific focus on testing for mediated and moderated effects. To complement the statistical analyses, fuzzy-set qualitative comparative analysis was also employed.

The path analysis revealed EK&S as primary drivers of EI and ESEA. Also, EK&S moderated the effects of FoF on EI, and the inclusion of EI improved the model significantly. The fuzzy-set qualitative comparative analysis result showed that the presence of EI, EK&S, FoF and SSE were sufficient but not necessary conditions for ESEA.

The tested model demonstrates the importance of EK&S and EI, as well as the need to mitigate the effects of the fear factor in promoting entrepreneurial activity. As such, the support of EK&S programmes seems justifiable.

The findings of this study provide a deeper insight into the intricate relationships that underlie entrepreneurial activity by utilising a combination of data analysis techniques.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.