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This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments.

A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms.

The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments.

This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio.

Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing.

This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

Aiding information is frequently adopted to calibrate the errors from inertia-generated trajectories in pedestrian positioning. However, existing calibration methods lack interior connections and unanimity, making it difficult to incorporate multiple sources of aiding information. This paper aims to propose a unanimous anchor-based trajectory calibration framework, which is expandable to encompass different types of anchor information.

The concept of anchors is introduced to represent different types of aiding information, which are, in essence, different constraint conditions on inertia-derived raw trajectories. The foundation of the framework is a particle filter which is implemented based on various particle weight updating strategies using diverse types of anchor information. Herein, three representative anchors are chosen to elaborate and validate the proposed framework, namely, ultra-wide-band (UWB) ranging anchors, iBeacons and the building structure-based virtual anchors.

In the simulations, with the particle reweighting strategies of the proposed framework, the positioning errors can be compensated. In the experimental test in an office building in which three anchors, including one UWB anchor, one iBeacon and one building structure-based virtual anchor are deployed; the final positioning error is decreased from 1.9 to 1.2 m; and the heading error is reduced from about 21° to 7°, respectively.

Herein, an anchor-based unanimous trajectory calibration framework for inertial pedestrian positioning is proposed. This framework is applicable to the schemes with different configurations of the anchors and can be expanded to adopt as much anchor information as possible.

At present, smartphones are embedded with accelerometers, gyroscopes, magnetometers and WiFi sensors. Most researchers have delved into the use of these sensors for localization. However, there are still many problems in reducing fingerprint mismatching and fusing these positioning data. The purpose of this paper is to improve positioning accuracy by reducing fingerprint mismatching and designing a weighted fusion algorithm.

For the problem of magnetic mismatching caused by singularity fingerprint, derivative Euclidean distance uses adjacent fingerprints to eliminate the influence of singularity fingerprint. To improve the positioning accuracy and robustness of the indoor navigation system, a weighted extended Kalman filter uses a weighted factor to fuse multisensor data.

The scenes of the teaching building, study room and office building are selected to collect data to test the algorithm’s performance. Experiments show that the average positioning accuracies of the teaching building, study room and office building are 1.41 m, 1.17 m, and 1.77 m, respectively.

The algorithm proposed in this paper effectively reduces fingerprint mismatching and improve positioning accuracy by adding a weighted factor. It provides a feasible solution for indoor positioning.

Teacher education and professional development have long been criticized for failing to induct and engage teachers in discourses of classroom deliberation. Lesson studies have contributed to an emerging discourse in which teachers come together to study classroom teaching to improve student learning. The purpose of this paper is to share knowledge about developing video resources to support this emerging discourse.

The authors showcase a digital hypermedia video case developed from research lessons on a third‐grade topic on division with remainder, conducted by teachers and researchers in a lesson study cycle in Singapore. Drawing on anchored instruction and knowledge points of the mathematics education discourse in China, the authors used embedded contexts, case‐based reasoning, critical incidents and facilitation as major design features.

A video documentary traces the research problem and how teachers learned to use the concrete‐pictorial‐abstract (CPA) model to improve teaching for student learning. Critical incidences are created to engage teachers in analyzing the research lessons by describing, interpreting and probing into the object of learning, student difficulties in learning, and how the teacher mediated the subject matter of teaching. A full range of lesson study data and related reading and web resources are provided in the video case to support training and self study.

The paper demonstrates the promise to capitalize on the curricular and pedagogical values of the rich video archives of lesson studies to support continued inquiry of teachers. It has important implications for addressing the issues of depth of implementation and sustainability arising from rapid spread of lesson studies in countries like Singapore.

The increasing global competition, worldwide economic and political uncertainities, and continuously changing dynamics in business environment require companies act differently and differentiate via smart strategies in order to have sustainable operations, growth, and profitability. Therefore, firms should be more agile, creative, and adaptive in planning and strategizing their mid- to long-term business objectives.

In that regard, for the last decade globally many firms across all industries seek opportunities to utilize benefits of digitalization. Lately, COVID-19 has also accelerated companies' efforts and investments in digital platforms.

Today, supply chain and procurement functions are expected to have a strategic role for organizations contributing to management decisions. The digital transformation in procurement is promising to enhance and lean the total workflow of operations. Data analytics, artificial intelligence, robotics, and other emerging digital technologies are all highly powerful tools supporting strategic supply and supplier management, providing predictability for demand planning as well as value-based negotiation power to buyers.

On the other hand, there are still challenges and conflicts throughout this transformation process. Level of technological maturity, infrastructure and investment decisions, expertise and competency of procurement professionals, cultural adaptation, and compliance of related stakeholders are some of the key barriers that are addressed with a unique model in this chapter.

Digital era offers a lot of advantages to firms to improve their procurement facilities and practices while it may still take time both for the technologies to fully evolve and also for companies to adapt and embrace digitalization on their benefit.

Purpose: This piece delves into the transformative potential of artificial intelligence (AI) in the healthcare field within the emerging realm of Industry 5.0, highlighting a people-focused and eco-friendly approach.

Need for the study: While Industry 4.0 set the foundation for digitization in healthcare, it frequently overlooked the human factor and concerns about sustainability. Industry 5.0 tackles these deficiencies by giving importance to human welfare, efficiency in resource usage, and societal consequences alongside technological progress.

Methodology: This research utilizes a survey of existing written works on Industry 5.0, AI in healthcare, and associated empowering technologies. It also leans on insights from recent investigations and business actions to pinpoint current patterns and future paths.

Findings: This chapter showcases how AI-driven solutions can greatly alter various facets of healthcare. Some of these healthcare facets encompass personalized medicine and treatment, intelligent diagnostics and decision support, robot-supported surgery and care, and enhanced availability and affordability.

Practical applications: This piece offers valuable perspectives for healthcare investors. These investors cover healthcare suppliers, technology creators, rule creators, and patients. By embracing the standards of Industry 5.0, the merging of AI into healthcare brings significant potential for crafting a more competent, sustainable, and people-centered healthcare network that benefits both patients and society as a complete unit. This research investigates the stance, viewpoints, and potential impacts of machine intelligence (MI) in health with an emphasis on Industry 5.0.

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

Many factors affect the emergence and development of crop diseases and insect pests. Traditional methods for investigating this subject are often difficult to employ and produce limited data with considerable uncertainty. The purpose of this paper is to predict the annual degree of cotton spider mite infestations by employing grey theory.

The authors established a GM(1,1) model to forecast mite infestation degree based on the analysis of historical data. To improve the prediction accuracy, the authors modified the grey model using Markov chain and BP neural network analyses. The prediction accuracy of the GM(1,1), Grey-Markov chain, and Grey-BP neural network models was 84.31, 94.76, and 96.84 per cent, respectively.

Compared with the single grey forecast model, both the Grey-Markov chain model and the Grey-BP neural network model had higher forecast accuracy, and the accuracy of the latter was highest. The improved grey model can be used to predict the degree of cotton spider mite infestations with high accuracy and overcomes the shortcomings of traditional forecasting methods.

The two new models were used to estimate mite infestation degree in 2015 and 2016. The Grey-Markov chain model yielded respective values of 1.27 and 1.15, whereas the Grey-BP neural network model yielded values 1.4 and 1.68; the actual values were 1.5 and 1.8.

The improved grey model can be used for medium- and long-term predictions of the occurrence of cotton spider mites and overcomes problems caused by data singularity and fluctuation. This research method can provide a reference for the prediction of similar diseases.

The practical need for understanding and improving team resilience has increased, and more research is needed to provide an evidence-base for guiding organizational practices and policies. In this chapter, the authors highlight what we see as critical challenges and opportunities for advancing the science of team resilience. We focus on conceptual and methodological challenges involved in conducting field-based research on team resilience, as the authors believe field-based research is a particularly critical approach for advancing the science of team resilience. The authors first provide a brief review of recent theoretical work in defining team resilience. Then the authors describe key challenges that must be managed in field studies seeking to refine and capitalize on this critical area of research to provide solutions capable of supporting individual, team, and organizational outcomes. These challenges include defining trajectories of resilient team performance, understanding the consequences of repeated episodes of team resilience, formal specifications of events precipitating resilient team performance, measuring the event appraisal and communication process, and adopting measurement methods with high temporal resolution. Finally, the authors provide directions for future research to address these gaps.

This chapter examines the way in which Industry 4.0 will revolutionize achieving the Sustainable Development Goals (SDGs). This study provides insights on the relationship between global sustainability goals and cutting-edge technologies like automation, artificial intelligence and the internet of things; it looks at how Industry 4.0 may spur innovation, job creation and economic growth while tackling environmental issues. Also, this study analyses how Industry 4.0 can be used as a catalyst for positive change, in line with the larger vision of a sustainable and inclusive future, from navigating obstacles like job displacement and cybersecurity risks to presenting opportunities through policy frameworks and stakeholder collaboration. The results of this chapter shed light on the usage of technology in addressing global issues like poverty, inequality, climate change and sustainable economic growth and in turn the achievement of SDGs. The study discusses in detail about the implications for policymakers on the impact of Industry 4.0 on SDG 8 and SDG 12 and the risks associated with using Industry 4.0 to achieve the SDGs like job displacement, cybersecurity risks and ethical and legal challenges. The managerial implications of this study are numerous including increasing the skilled workforce and enhancing transparency, traceability and environmental performance across the whole supply chain. The study finally concludes by examining the potential prospects and future trends of Industry 4.0 and its integration with the SDGs.