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The use of a designed quality plan for application in the electrical power industry is presented. Some contributions of Taguchi’s technique in power process parameter design are reviewed. Recommendations are made for developing a quality‐training plan that will incorporate the design of experiments and description of training sources. A case study of the experiment design in the Hellenic power production process is discussed. Difficulties with design of experiments applications to the power process are outlined and suggestions are offered for resolving these difficulties.

Safety research has focused on drivers, pedestrians and vehicles, with scarce attention given to highway traffic officers (HTOs). This paper develops a robust prediction model which enables highway safety authorities to predict exclusive incidents occurring on the highway such as incursions and environmental hazards, respond effectively to diverse safety risk incident scenarios and aid in timely safety precautions to minimise HTO incidents.

Using data from a highway incident database, a supervised machine learning method that employs three algorithms [namely Support Vector Machine (SVM), Random Forests (RF) and Naïve Bayes (NB)] was applied, and their performances were comparatively analysed. Three data balancing algorithms were also applied to handle the class imbalance challenge. A five-phase sequential method, which includes (1) data collection, (2) data pre-processing, (3) model selection, (4) data balancing and (5) model evaluation, was implemented.

The findings indicate that SVM with a polynomial kernel combined with the Synthetic Minority Over-sampling Technique (SMOTE) algorithm is the best model to predict the various incidents, and the Random Under-sampling (RU) algorithm was the most inefficient in improving model accuracy. Weather/visibility, age range and location were the most significant factors in predicting highway incidents.

This is the first study to develop a prediction model for HTOs and utilise an incident database solely dedicated to HTOs to forecast various incident outcomes in highway operations. The prediction model will provide evidence-based information to safety officers to train HTOs on impending risks predicted by the model thereby equipping workers with resilient shocks such as awareness, anticipation and flexibility.

In today’s competitive era, it has become significant for companies to understand their end consumer and target customers effectively. One of the ways to accomplish this goal is data-driven marketing. The current study seeks to explore the differences between traditional marketing and digital marketing, the pros and cons of data-driven marketing and usage of artificial intelligence (AI) in data-driven marketing. The research objective was met by exploration of published papers in the past 10 years covering the evolution of data-driven marketing, functions of data engineering, application of technology like AI in data-driven marketing and opportunities and challenges. This study is significant as it provides the insight into the relationship between marketing and data engineering and thus helps marketers to frame strategies by leveraging data-driven marketing to improve consumer experience and gain a competitive edge. Moreover, this study is an interdisciplinary study including marketing, engineering and data science. This study focusses on use of innovative methods to improve profitability of business and consumer experience.

Casting is one of the well-known manufacturing processes to make durable parts of goods and machinery. However, the quality of the casting parts depends on the proper choice of process variables related to properties of the materials used in making a mold and the product itself; hence, variables related to product/process designs are taken into consideration. Understanding casting techniques considering significant process variables is critical to achieving better quality castings and helps to improve the productivity of the casting processes. This study aims to understand the computational models developed for achieving better quality castings using various casting techniques.

A systematic literature review is conducted in the field of casting considering the period 2000–2020. The keyword co-occurrence network and word cloud from the bibliometric analysis and text mining of the articles reveal that optimization and simulation models are extensively developed for various casting techniques, including sand casting, investment casting, die casting and squeeze casting, to improve quality aspects of the casting's product. This study further investigates the optimization and simulation models and has identified various process variables involved in each casting technique that are significantly affecting the outcomes of the processes in terms of defects, mechanical properties, yield, dimensional accuracy and emissions.

This study has drawn out the need for developing smart casting environments with data-driven modeling that will enable dynamic fine-tuning of the casting processes and help in achieving desired outcomes in today's competitive markets. This study highlights the possible technology interventions across the metal casting processes, which can further enhance the quality of the metal casting products and productivity of the casting processes, which show the future scope of this field.

This paper investigates the body of literature on the contributions of various researchers in producing high-quality casting parts and performs bibliometric analysis on the articles. However, research articles from high-quality journals are considered for the literature analysis in identifying the critical parameters influencing quality of metal castings.

The systematic literature review reveals the analytical models developed using simulation and optimization techniques and the important quality characteristics of the casting products. Further, the study also explores critical influencing parameters involved in every casting process that significantly affects the quality characteristics of the metal castings.

The aim of this paper is to predict and minimize the risks of oil, gas and petrochemical projects. Besides, reducing the likelihood of occurrence and minimizing risks impact on the projects to reduce the probable costs and improve the economic situation is another purpose of this paper.

This paper provides a fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) – a technique that assist to solve decision-making problems – and IP (Impact & Probability) table methods to identify and analyze critical risks in energy projects, and then fuzzy Binary Logistic Regression (BLR) in order to predict the probability of each level of risk for more efficient risk management in projects. Furthermore, in this paper, the fuzzy BLR (FBLR) is optimized such that the probability of a high level of risk for the implementation of the project has been minimized using meta-heuristic algorithm.

The results from the point of view of experts show that combination of fuzzy DEMATEL with FBLR approach as well as using SA algorithm, in order to optimize the high level of risks, can provide a smart approach to managing risks with more success.

The application of the proposed method is illustrated via a real data set from energy projects.

We propose combined fuzzy DEMATEL and FBLR methods to predict and optimize the risks of the energy projects, which is the innovation of this paper.

Crowdfunding creates multifaceted benefits for different agents who all desire to extract some of these benefits. The purpose of this paper is to analyze the allocation of crowdfunding benefits among crowdfunders, entrepreneurs, and venture capitalists.

The present paper develops a multi-stage bargaining model with a double-sided moral hazard.

It is demonstrated that higher entrepreneurial bargaining power vis-à-vis the crowd may not always be beneficial for the venture. Most importantly, this is due to the reduced success probability of crowdfunding resulting from higher bargaining power of the entrepreneur. Bargaining power and the value of outside options determine the equilibrium allocation of crowdfunding benefits, expected venture value, and thus expected wealth of all agents.

Entrepreneurs face a tradeoff between venture quality gains and worse outcomes from crowdfunding campaigns. Crowdfunding success and thus venture quality gains are the ultimate goal of policy makers if they aim to enhance the overall social welfare.

This paper is the first to investigate how multifaceted crowdfunding benefits are allocated between the crowd, entrepreneurs, and venture capitalists. The paper furthers the development of an appropriate regulatory framework for crowdfunding by depicting new and original effects related to crowdfunding.

The aim of the study is to propose certain agricultural policy measures for the financing and development of Greek farms, established by young farmers, based on the results of a clustering method suitable for handling socio‐economic categorical data.

The clustering method was applied to categorical data collected from 110 randomly selected investment plans of Greek agricultural farms. The investment plans were submitted to the “Region of Central Macedonia” administrative office, in the framework of the Operational Programme “Agricultural Development – Reform of the Countryside 2000‐2006” and refer to agricultural investments by “Young Farmers”, according to the terms and conditions of Priority Axis III: “Improvement of the Age Composition of the Agricultural Population”. The input variables for the analyses were the farmers' gender, age class, education level and permanent place of residence, the farms' agricultural activity, Human Labour Units (HLU) and farms' viability level. All these variables were measured on nominal or ordinal scales. The available data were analyzed by means of a hierarchical cluster analysis method applied on the rows of an appropriate matrix of a complete disjunctive form with a dummy coding 0 or 1. The similarities were measured through the Benzécri'sχ²distance (metric), while the Ward's method was used as a criterion for cluster formation.

Five clusters of farms emerged, with statistically significant diverse socio‐economic profiles. The most important impact on the formation of the groups of farms was found to be related to the number of HLU, the farmers' level of education and gender. This derived typology allows for the determination of a flexible development and funding policy for the agricultural farms, based on the socio‐economic profile of the formulated clusters.

One of the limitations of the current study derives from the fact that the clustering method used is suitable only for categorical, non‐metric data. Another limitation comes from the fact that a relative small number of investment plans were used in the analysis. A larger sample covering and other geographical regions is needed in order to confirm the current results and make nation‐wide comparisons and “tailor‐made” proposals for financing and development. Finally, it is interesting to contact longitudinal surveys in order to evaluate the effectiveness of the funding policy of the corresponding programme.

The study's results could be useful to practitioners and academics because certain agricultural policy measures for the financing and development of Greek farms established by young farmers are proposed. Additionally, the data analysis method used in this study offers an alternative way for clustering categorical data.

The purpose of this paper is to develop initial evidence about the nature and features of crowdfunding in China, given it is largely unregulated regulatory frameworks.

The paper used extensive desk research using data collected from the public and private sectors, after which the data was analyzed parallel to existing academic literature, that is, institutional context by Bruton et al. (2014). This paper uncovered patterns of development, profiling crowdfunding platforms, examining the regulatory landscape and providing antecedents of successful crowdfunding projects in China.

When the traditional financial markets are hard to reach, micro, small and medium enterprises (MSMEs) were starved for capital. Crowdfunding can play a major role in funding and risk sharing. It is an innovative and dynamic vehicle for MSMEs as well as enthusiastic investors in China. Since its initial introduction to China in 2009, crowdfunding has gained substantial popularity in a relatively short period. Currently, there is still not an identifiable guideline on how to delineate the significance of the crowdfunding platform. The development of crowdfunding in China faces a few unresolved key issues. As researchers exploring this phenomenon in new ways, crowdfunding platforms can be enhanced in a manner that benefits the capital seeker, investors and society as a whole.

There is a dearth of information on start-up crowdfunding in Asia. With little data available to analyze, so this paper hopes to contribute to knowledge and provide valuable information to researchers and industry representations. Crowdfunding represents a potentially disruptive change in the way that new ventures are funded. This paper represents an initial analysis in the study of new ventures in China. Finally, the authors provide recommendations for entrepreneurs, investors and policymakers as well as researchers and practitioners with suggestions about yet unexplored avenues of research.

This paper aims to investigate the importance of auditing for election privacy via issues that appear in the state-of-the-art implementations of e-voting systems that apply threshold public key encryption (TPKE) in the client such as Helios and use a bulletin board (BB).

Argumentation builds upon a formal description of a typical TPKE-based e-voting system where the election authority (EA) is the central node in a star network topology. The paper points out the weaknesses of the said topology with respect to privacy and analyzes how these weaknesses affect the security of several instances of TPKE-based e-voting systems. Overall, it studies the importance of auditing from a privacy aspect.

The paper shows that without public key infrastructure (PKI) support or – more generally – authenticated BB “append” operations, TPKE-based e-voting systems are vulnerable to attacks where the malicious EA can act as a man-in-the-middle between the election trustees and the voters; hence, it can learn how the voters have voted. As a countermeasure for such attacks, this work suggests compulsory trustee auditing. Furthermore, it analyzes how lack of cryptographic proof verification affects the level of privacy that can be provably guaranteed in a typical TPKE e-voting system.

As opposed to the extensively studied importance of auditing to ensure election integrity, the necessity of auditing to protect privacy in an e-voting system has been mostly overlooked. This paper reveals design weaknesses present in noticeable TPKE-based e-voting systems that can lead to a total breach of voters’ privacy and shows how auditing can be applied for providing strong provable privacy guarantees.

The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy.

The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services.

The proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain.

This study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.