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Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

This chapter investigates a new human-centric perspective oriented to consider society as a whole, by following a systemic vision, where social well-being is important: Industry 5.0. The analysis is based on the great advantages for the ecosystem, going beyond the custom of affirming technological development and managerial views, favoring the dialog between the actors of the system, the adaptation of technologies to the context in which one operates, choosing the most appropriate business strategies, sustaining smart, sustainable and inclusive solutions in the innovation process. In this sense, big data, internet of things, augmented realities, or robotics are all technologies to increase productivity and improve working conditions but, at the same time, they have to improve social life. To overcome the main related challenges and support the transition, new insights to design innovative strategies urge. Our research aims to explore the scientific literature about Industry 5.0 and its development in the European Union. We carried out a systematic review and a bibliometric analysis of the scientific production through the Web of Science (WoS) and SCOPUS databases. In addition, our study analyzes how the proposed topic has evolved over time by collecting contributions from different countries and authors. We adopted an innovative tool that goes beyond the traditional statistical methods generally used in bibliometric research, and it allowed us to obtain more advanced results. Our software permitted us to join the findings of two databases simultaneously, such as WoS and SCOPUS, to generate a bibliometric analysis and identify new paradigm trends.

In this chapter, we explain the significance and need for a spiritual approach among techies that would help them be human-centric, compassionate and value-based for sustainable development. We introduced four perspectives of workplace spirituality, higher purpose, interconnectedness, meaningfulness and mindfulness, as significant indicators of sustainable behaviour among the techies. Finally, we discuss how a spiritual approach could help techies contribute to sustainable development. We contribute to the literature by elucidating the role of spirituality among techies that could help advance sustainable technological development and techie's well-being.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

In order to better optimize the internal management system of book publishing and to cope with the changes in the external market environment, the purpose of this paper is to carry out cross-border publishing with the help of a transmedia storytelling model to realize the transformation and upgrading of the industry. Focusing on the relationship between the book publishing transmedia storytelling model and business performance, the moderating effect of the innovation environment on different variables is assessed.

This paper proposes several feasible hypotheses based on existing research. The research data came from 365 managers of Chinese book publishing organizations, and the scale was validated by Cronbach’s a, composite reliability (CR) and average variance extracted (AVE). Reliability and validity were verified, and correlation and regression analyses were used to test the impact of the book publishing transmedia storytelling model on business performance and to analyze the moderating role of the innovation environment.

The results show that the book publishing transmedia storytelling model (content production, technology integration, organizational innovation, marketing integration) helps to improve business performance (market performance, financial performance), and the innovation environment has a positive moderating effect on the relationship between the book publishing transmedia storytelling model and business performance, which provides a guarantee for the transformation and upgrading of book publishing. The market information reflected in the innovation environment has a certain role in promoting the innovation and business performance of the book publishing transmedia storytelling model.

The empirical evidence provides a theoretical link between the book publishing transmedia storytelling model and business performance, but there are still some shortcomings, and more factors, such as equity structure, government subsidies and research and development investment, should be included in future research. In addition, the scope of the research should be broadened on this basis to make the results of the data analysis more objective.

This paper introduces the transmedia storytelling model and deeply analyzes the relationship between the book publishing transmedia storytelling model and business performance, which is of great practical significance for optimizing the application and service quality of book publishing, prolonging the industrial chain, enhancing the interaction and participation of users and perfecting the business management system of the book publishing industry.

The application and research of the book publishing transmedia storytelling model are imperfect. Therefore, this paper not only helps to promote the innovation of book publishing organizational structure and improve the management system of business performance, but also may help to improve the innovation environment of book publishing enterprises and promote the diversification of industrial structure.

This study aims to solve the problem that under light-load conditions, the output voltage regulation capability is lost due to the fact that the voltage gain of the LLC resonant converter does not decrease with the increase of the switching frequency.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated under light-load conditions, the limitations of the previous method are explained and a new circuit improvement is proposed.

In this paper, an improved circuit is proposed, and the impedance Bode plot is used to verify that the circuit can effectively improve the voltage gain problem under light-load conditions. Finally, the experimental results verify the effectiveness of the proposed circuit through comparison with traditional solutions and circuits.

In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated, the limitations of the previous method are explained and a new circuit improvement is proposed. When compared with the previous method, the proposed circuit improvement can suppress the voltage gain increase that occurs when the switching frequency increases to a certain level.

This study aims to achieve optimum flow separation control for a road vehicle using a reverse flow fan on rear side.

A full-length reverse flow fan array (fan’s air speed is 50% of the car’s speed) is attached throughout the width of the vehicle at rear edge corner.

The reverse flow fan array positioned at rear edge of car pushes the airflow against the car’s rear window. It creates the recirculation region and alters the pressure distribution. This reduces the lift coefficient by 150%, which becomes the downforce and reduces the drag coefficient by 22%. As the car speed increases, fan speed should also be increased for effective flow control.

This active flow control method for 3D Ahmed car body has been studied computationally at low speed (40 m/s).

This design increases the downforce, thus gives better cornering speed and stability, and decreases the drag which improves fuel efficiency. It can be used for effective flow control of cars (hatchback/sedan). The findings can be applied to the bluff bodies, road vehicles, UAV and helicopter fuselage for flow separation control.

The fan array is attached on car’s rear side, which blows air against the car’s rear window. It alters the pressure distribution and aerodynamics forces favorably. But the existing high-speed fan used in a sports cars sucks the air from bottom and pushes it rearward, which increases both the traction force and drag.

A direct correlation exists between waste disposal, disease spread and public health. This article systematically reviewed healthcare waste and its implication for public health. This review identified and described the associations and impact of waste disposal on public health.

This paper systematically reviewed the literature on waste disposal and its implications for public health by searching Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), PubMed, Web of Science, Scopus and ScienceDirect databases. Of a total of 1,583 studies, 59 articles were selected and reviewed.

The review revealed the spread of infectious diseases and environmental degradation as the most typical implications of improper waste disposal to public health. The impact of waste includes infectious diseases such as cholera, Hepatitis B, respiratory problems, food and metal poisoning, skin infections, and bacteremia, and environmental degradation such as land, water, and air pollution, flooding, drainage obstruction, climate change, and harm to marine and wildlife.

Infectious diseases such as cholera, hepatitis B, respiratory problems, food and metal poisoning, skin infections, bacteremia and environmental degradation such as land, water, and air pollution, flooding, drainage obstruction, climate change, and harm to marine and wildlife are some of the public impacts of improper waste disposal.

Healthcare industry waste is a significant waste that can harm the environment and public health if not properly collected, stored, treated, managed and disposed of. There is a need for knowledge and skills applicable to proper healthcare waste disposal and management. Policies must be developed to implement appropriate waste management to prevent public health threats.

Given the growing preference for internet celebrity restaurants, it is crucial to explore how internet celebrity restaurants can maintain customer loyalty. Therefore, this study aims to examine the connections between brand cognition [emotion value, brand symbol (BS) and brand experience (BE)], brand resonance (BR) and revisit intention.

In this paper, the authors use a theoretical model to test the relationship between cognition and intention. A total of 366 volunteers were recruited to participate in this research. Hypothesis testing and a moderated mediation model were used to measure the results.

BR acts as a mediator in the interaction between emotion value, BS, experience and repurchase intention (RI). Surprisingly, the authors also discover that electronic word-of-mouth (e-WOM) acceptance negatively modifies the relationship between brand cognition and BR. Internet exposure (IE) helps consumers perceive BE and BSs more favorably.

Managers should be aware of how internet celebrity BR is built. Specifically, they can use cultural or emotional elements to maintain relationships with consumers. Furthermore, to lessen the negative consequences of e-WOM, managers should work to maintain positive WOM consistency.

The research advances our knowledge of RI in internet celebrity restaurants settings. This study pioneers an investigation of how brand cognition is related to RI through BR’s mediating effect. It enriches this research perspective of the emerging restaurant literature. By analyzing the boundary impact of internet transmission on resonance, it also advances the literature.

Fake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.

The authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.

The authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.

The authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.