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The purpose of this study is to evaluate the effect of graphene, basalt flakes and their synergy on the corrosion resistance of zinc-rich coatings. As the important heavy-duty anticorrosion coatings, zinc-rich coatings provided cathodic protection for the substrate. However, to ensure cathodic protection, a large number of zinc powder made the penetration resistance known as the weakness of zinc-rich coatings. Therefore, graphene and basalt flakes were introduced into zinc-rich coatings to coordinate its cathodic protection and shielding performance.

Three kinds of coatings were prepared; they were graphene modified zinc-rich coatings, basalt flakes modified zinc-rich coatings and graphene-basalt flakes modified zinc-rich coatings. The anticorrosion behavior of painted steel was studied by using the electrochemical impedance spectroscopy (EIS) technique in chloride solutions. The equivalent circuit methods were used for EIS analysis to obtain the electrode process structure of the coated steel system. Simultaneously, the corrosion resistance of the three coatings was evaluated by water resistance test, salt water resistance test and salt spray test.

The study found that the addition of a small amount of graphene and basalt flakes significantly improved the anticorrosion performance of coatings by enhancing their shielding ability against corrosive media and increasing the resistance of the electrochemical reaction. The modified coatings exhibited higher water resistance, salt water resistance and salt spray resistance. The graphene-basalt flakes modified zinc-rich coatings demonstrated the best anticorrosion effect. The presence of basalt scales and graphene oxide in the coatings significantly reduced the water content and slowed down the water penetration rate in the coatings, thus prolonging the coating life and improving anticorrosion effects. The modification of zinc-rich coatings with graphene and basalt flakes improved the utilization rate of zinc powder and the shielding property of coatings against corrosive media, thus strengthening the protective effect on steel structures and prolonging the service life of anticorrosion coatings.

The significance of developing graphene-basalt flakes modified zinc-rich coatings lies in their potential to offer superior performance in corrosive environments, leading to prolonged service life of metallic structures, reduced maintenance costs and a safer working environment. Furthermore, such coatings can be used in various industrial applications, including bridges, pipelines and offshore structures, among others.

Introduction: The insurance sector provides security to society by pooling resources to manage risks. Insurers’ improved ability to analyse risks by examining vast amounts of granular data has considerably refined this technique. Compiling and analysing the fine data sets is now transformed into the ‘Big Data’ technique. The introduction of big data analytics (BDA) is transforming the insurance industry and the role data plays in insurance.

Purpose: This chapter will attempt to examine the applications and role of big data in the insurance sector and how big data affects the different insurance segments like health insurance, property and casualty, and travel insurance. This chapter will also describe the disruptive impact of big data on the insurance market.

Methodology: Systematic research is carried out by analysing case studies and literature studies, emphasising how BDA is revolutionary for the insurance market. For this purpose, various articles and studies on BDA in the insurance market are selected and studied.

Findings: The execution of big data is continuously increasing in the insurance sector. The performance of big data in the insurance market results in cost reduction, better access to insurance services, and more fraud detection that benefits the customers and stakeholders. Therefore, big data has revolutionised the insurance market and assisted insurers in targeting customers more precisely.

The purpose of this study is to present a methodology for parameters’ sensitivity analysis of solar-powered airplanes.

The study focuses on a preliminary design and parameters’ relations of a heavier-than-air, solar-powered, high-altitude long-endurance unmanned aerial vehicle. The methodology is founded on the balance of energy production and requirement. An analytic expression with four generalized parameters is derived to determine the airplane flying on the specific altitude. The four generalized parameters’ sensitivities on altitude are then analyzed. Finally, to demonstrate the methodology, a case study is given on the parameters’ sensitivity analysis of a prototype solar-powered airplane.

When using the presented methodology, the nighttime duration and the energy density of batteries are more sensitive on flight altitude of the prototype airplane.

It is not easy to design a solar-powered airplane to realize high-attitude and long-endurance flight. For the current state-of-art, it is a way to figure out the most critical parameters which need prior consideration and immediate development.

This paper provides an analytical methodology for analyzing the parameters’ sensitivities of solar-powered airplanes, which can benefit the preliminary design of a solar-powered airplane.

Introduction: The Internet has tremendously transformed the computer and networking world. Information reaches our fingertips and adds data to our repository within a second. Big data was initially defined as three Vs, where data come with greater variety, increasing volumes and extra velocity. Big data is a collection of structured, unstructured and semi-structured data gathered from different sources and applications. It has become the most powerful buzzword in almost all the business sectors. The real success of any industry can be counted based on how the big data is analysed, potential knowledge is discovered and productive business decisions are made. New technologies such as artificial intelligence and machine learning have added more efficiency to storing and analysing data. This big data analytics (BDA) becomes more valuable to those companies, focusing on getting insight into customer behaviour, trends and patterns. This popularity of big data has inspired insurance companies to utilise big data at their core systems and advance the financial operations, improve customer service, construct a personalised environment and take all possible measures to increase revenue and profits.

Purpose: This study aims to recognise what big data stands for in the insurance sector and how the application of BDA has opened the door for new and innovative changes in the insurance industry.

Methodology: This study describes the field of BDA in the insurance sector, discusses the benefits, outlines tools, architectural framework, the method, describes applications in general and specific and briefly discusses the opportunities and challenges.

Findings: The study concludes that BDA in insurance is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however, there remain challenges to overcome.

Introduction: Big data is that disruptive force that affects businesses, industries, and the economy. In 2021, insurance analytics will include more than simply analysing statistics. According to current trends, new insurance big data analytics (BDA) methods will enable firms to do more with their data. The insurance business has traditionally been conservative, but adopting new technology is no longer only a current trend; it must be competitive. Big data technologies aid in processing a huge amount of data, improve workflow efficiency, and lower operating costs.

Purpose: Some of the most recent developments in big data for insurance and how insurers may use the information to stay ahead of their competitors are discussed in this chapter. This chapter’s prime purpose is to analyse how artificial intelligence (AI), blockchain, and mobile technology change the outlook and working of the insurance sector.

Methodology: To achieve our research purpose, we analyse case studies and literature that emphasise how BDA revolutionises the insurance market. For this purpose, various articles and studies on BDA in the insurance market will be selected and studied.

Findings: From the analysis, we find that the use of big data in the insurance business is growing. The development of BDA has proven to be a game-changing technology in insurance, with a slew of benefits. The insurance sector is now grappling with the risks and opportunities that modern technology presents. Big data offers opportunities that every company must avail of. We can safely argue that big data has transformed the insurance sector for the better. The BDA’s consequences have enabled insurers to target clients more accurately. This chapter highlights that new tools and technologies of big data in the insurance market are increasing. AI is emerging as a powerful technology that can alter the entire insurance value stream. The transmission of any type of digital proof for underwriting, including the use of digital health data, might be a blockchain use case (electronic health record (EHR)). As digital forensics becomes easier to include in underwriting, it must expect price and product design changes in the future. In the future, the internet of things (IoT) and AI will combine to automate insurance processes, causing our sector to transform dramatically. We highlight that these technologies transformed insurance practices and revolutionalised the insurance market.

This chapter reviews models of decision-making and choice under conditions of certainty. It allows readers to position the contribution of the other chapters in this book in the historical development of the topic area.

Bounded rationality is defined in terms of a strategy to simplify the decision-making process. Based on this definition, different models are reviewed. These models have assumed that individuals simplify the decision-making process by considering a subset of attributes, and/or a subset of choice alternatives and/or by disregarding small differences between attribute differences.

A body of empirical evidence has accumulated showing that under some circumstances the principle of bounded rationality better explains observed choices than the principle of utility maximization. Differences in predictive performance with utility-maximizing models are however small.

The chapter provides a detailed account of the different models, based on the principle of bounded rationality, that have been suggested over the years in travel behaviour analysis. The potential relevance of these models is articulated, model specifications are discussed and a selection of empirical evidence is presented. Aspects of an agenda of future research are identified.

Introduction: In recent years, fresh big data ideas and concepts have emerged to address the massive increase in data volumes in several commercial areas. Meanwhile, the phenomenal development of internet use and social media has not only added to the enormous volumes of data available but has also posed new hurdles to traditional data processing methods. For example, the insurance industry is known for being data-driven, as it generates massive volumes of accumulated material, both structured and unstructured, that typical data processing techniques can’t handle.

Purpose: In this study, the authors compare the benefits of big data technologies to the needs for insurance data processing and decision-making. There is also a case study evaluation concentrating on the primary use cases of big data in the insurance business.

Methodology: This chapter examines the essential big data technologies and tools from the insurance industry’s perspective. The study also included an analytical analysis that supported several gains made by insurance companies, such as more efficient processing of large, heterogeneous data sets or better decision-making support. In addition, the study examines in depth the top seven use cases of big data in insurance and justifying their use and adding value. Finally, it also reviewed contemporary big data technologies and tools, concentrating on their key concepts and recommended applications in the insurance business through examples.

Findings: The study has demonstrated the value of implementing big data technologies and tools, which enable the development of powerful new business models, allowing insurance to advance from ‘understand and protect’ to ‘predict and prevent’.

To answer the questions: what roles windows of opportunity act in the catchup process of latecomers, what strategies latecomer enterprises should adopt to size windows of opportunity to catch-up with incumbents even going beyond?

This paper studies the catch-up history of the Chinese mobile phone industry and proposes a sectoral innovation system under scenario of technology paradigm shifts. Then a history-friendly simulation model and counterfactual analysis are conducted to learn how different windows of opportunity and catch-up strategies influence the catch-up performance of latecomers.

Results show latecomers can catch up with technology ability by utilizing technology window and path-creating strategy. However, catching up with the market is not guaranteed. Demand window can help latecomers to catch up with market as it increases their survival rates, different sized windows benefit different strategies. However, it also enlarges incumbents' scale effect. Without technology window technology catch up is not guaranteed. Two windows have combination effects. Demand window affects the “degree” of change in survival rates, while the technology window affects the “speed” of change. Demand window provides security; technology window provides the possibility of a breakthrough for technology ability.

The findings of this paper provide theoretical guidance for latecomer enterprises to choose appropriate catch-up strategies to seize different opportunity windows.

This paper emphasizes the abrupt change of industrial innovation system caused by technology paradigm shifts, which makes up for the shortcomings of previous researches on industrial innovation system which either studied the influence of static factors or based on the influence of continuous changes.

The purpose of this paper is to investigate three issues: how does an innovative search (local search and boundary-spanning search) impact firm innovation performance of latecomers; how does capability reconfiguration (capability evolution and capability substitution) mediates the relationship between innovative search and firm innovation performance; and how does the technological leapfrogging process (initial stage, following stage, synchronization stage and leading stage) moderate the relationship between capability reconfiguration and firm innovation performance.

A “resource-capability-performance” theoretical framework was developed to explore the relationships between local/boundary-spanning search, capability reconfiguration and firm innovation performance. The data were collected by sending out surveys to managers and employees in various industries in mainland China. These hypotheses were tested using structural equation models and hierarchical regressions.

The results showed that: innovative search has a direct causal relationship to capability reconfiguration; local search and boundary-spanning search are conducive to improve the innovation performance of latecomers; the impact of local search and boundary-spanning search on innovation performance is realized through the completion of mediating role of capability reconfiguration; there are differences in the path of local search and boundary-spanning search affecting the capability reconfiguration of enterprise innovation performance; and the relationship between innovative search, capability reconfiguration and enterprise innovation performance evolves with the enterprise in different stages of technological leapfrogging.

This study explores the relationship and the path of innovative search to firm innovation performance and analyzes the path difference between local search and boundary-spinning search, which enriches the research of organizational search and enterprise innovation. This paper reveals the whole path of innovative search affecting innovation performance, discusses the important role of capability reconfiguration and makes incremental contributions to dynamic capability theory. It studies the evolution of innovative search on innovation performance under the background of technological leapfrogging, which provides a new perspective for the study of organizational search and capability-based theory.

The purpose of this paper is to study a transportation service procurement bid construction problem from a less than a full truckload perspective. It seeks to establish stochastic mixed integer programming to allow for the proper bundle of loads to be chosen based on price, which could improve the likelihood that carrier can earn its maximum utility.

The authors proposes a bi-level programming that integrates the bid selection and winner determination and a discrete particle swarm optimization (PSO) solution algorithm is then developed, and a numerical simulation is used to make model and algorithm analysis.

The algorithm comparison shows that although GA could find a little more Pareto solutions than PSO, it takes a longer time and the quality of these solutions is not dominant. The model analysis shows that compared with traditional approach, our model could promote the likelihood of winning bids and the decision effectiveness of the whole system because it considers the reaction of the shipper.

The highlights of this paper are considering the likelihood of winning the business and describing the conflicting and cooperative relationship between the carrier and the shipper by using a stochastic mixed integer programming, which has been rarely examined in previous research.