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Blockchain technology and its applications have recently received the attention of practitioners and academics. Visualizing the full impact these technologies will have on the world is challenging since their adoption is still in the early stages. This chapter explores how blockchain can disrupt the general business and financial world. Blockchain offers information transparency, live data synchronization, and immutable records that prevent fraud. Digital contracts and digital asset management may heavily depend on the development of blockchain and the adoption of smart contracts. Smart contracts can execute financial transactions automatically and enforce all parties' obligations without needing an intermediary and its cost. They can increase speed and simplify processes, reducing licensing ticketing costs and overhead charges. Blockchain also offers technology adoption solutions, like fair payment, cloud storage, smart contracts for financial assets, and international transactions with bilateral double taxation. It has further facilitated the creation of decentralized finance.

Security in large-scale agile is a crucial aspect that should be carefully addressed to ensure the protection of sensitive data, systems and user privacy. This study aims to identify and characterize the security practices that can be applied in managing large-scale agile projects.

A qualitative study is carried out through 18 interviews with 6 software development companies based in Portugal. Professionals who play the roles of Product Owner, Scrum Master and Scrum Member were interviewed. A thematic analysis was applied to identify deductive and inductive security practices.

The findings identified a total of 15 security practices, of which 8 are deductive themes and 7 are inductive. Most common security practices in large-scale agile include penetration testing, sensitive data management, automated testing, threat modeling and the implementation of a DevSecOps approach.

The results of this study extend the knowledge about large-scale security practices and offer relevant practical contributions for organizations that are migrating to large-scale agile environments. By incorporating security practices at every stage of the agile development lifecycle and fostering a security-conscious culture, organizations can effectively address security challenges in large-scale agile environments.

Harmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives.

In the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault.

In contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment.

The authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

From a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply chain can not only broaden the scope of business but also increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects. How to coordinate multiple circular economy supply chain projects is at the core of its operation.

This paper first analyzes some typical supply chain projects in China and summarizes the main features of these projects. Secondly, considering the benefits of the project and the stakes of each project, a multi-stage stochastic programming model is established. Finally, Cplex, nested decomposition, LocalSolver and other methods are adopted to simulate and analyze the model.

The final experimental results find that the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

There are still some limitations to this study; for example, it is limited to the analysis of circular economy supply chain projects in China. The study focused on third-party logistics companies, and other enterprises in the circular economy supply chain were not considered. The research also assumed that the benefits of each circular economy project and the stakes of each project were known, which may not always be the case in real-world scenarios.

This manuscript found that investing in other circular economy projects in the supply chain can broaden the scope of business and increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects, such as recycling and repurposing initiatives. It highlights the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

Computer vision (CV) offers a promising approach to transforming the conventional in-person inspection practices prevalent within the construction industry. However, the reliance on centralized systems in current CV-based inspections introduces a vulnerability to potential data manipulation. Unreliable inspection records make it challenging for safety managers to make timely decisions to ensure safety compliance. To address this issue, this paper proposes a blockchain (BC) and CV-based framework to enhance safety inspections at construction sites.

This study adopted a BC-enhanced CV approach. By leveraging CV and BC, safety conditions are automatically identified from site images and can be reliably recorded as safety inspection data through the BC network. Additionally, by using this data, smart contracts coordinate inspection tasks, assign responsibilities and verify safety performance, managing the entire safety inspection process remotely.

A case study confirms the framework’s applicability and efficacy in facilitating remote and reliable safety inspections. The proposed framework is envisaged to greatly improve current safety inspection practices and, in doing so, contribute to reduced accidents and injuries in the construction industry.

This study provides novel and practical guidance for integrating CV and BC in construction safety inspection. It fulfills an identified need to study how to leverage CV-based inspection results for remotely managing the safety inspection process using BC. This work not only takes a significant step towards data-driven decision-making in the safety inspection process, but also paves the way for future studies aiming to develop tamper-proof data management systems for industrial inspections and audits.

Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.

To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.

We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.

The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.

With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.

This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.

Integrating digital technologies such as artificial intelligence and blockchain make the agri-food supply chain (ASCM) transparent, resilient and flexible. However, its adoption is quite complex due to various anticipated barriers. So, the presented research purposes to explore and investigate the barriers.

This study employs hybrid approach including Best-Worst Method (BWM) and Graph Theoretic Approach (GTA). Data were collected from industry experts employed in the agri-food sector and analyzed by means of standard operating procedures.

GTA results show that Technological barriers have the highest barrier intensity. Moreover, BWM results show that “Increased operational complexity” is the topmost barrier to adopting blockchain in ASCM. “Lack of interoperability” ranks second among the identified barriers.

The results benefit the managers, practitioners and researchers to understand the anticipated barriers so that necessary strategies can be developed, and organizations can become more resilient, agile, transparent and traceable.

The presented work is the first to develop a mathematical model and assess the industry’s eagerness to adopt blockchain in ASCM. The proposed framework will greatly benefit the stakeholders working in agri-food sector.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

This study aims to identify mutually exclusive risk categories and determine whether these categories effectively capture the potential impact of risk disclosures on the initial returns of initial public offerings (IPOs) in the financial and non-financial sectors.

Data were collected from 131 Indian IPO prospectuses (104 non-financial and 27 financial) issued between 2015 and 2021. Content analysis was performed to identify mutually exclusive risk categories, and the effects of these categories on initial IPO returns were assessed by regression analysis

The findings revealed that risk factor disclosures have a significant impact on underpricing, but not all risk factors are relevant. In the current study, in the financial sector, IPO underpricing was mostly driven by technological and competitive risk factors. In the non-financial sector, underpricing was predominantly influenced by operating risk and compliance risk factors.

The limitations of this study include the use of sentence-based context analysis, which does not assess the quality of risk disclosures. The statistical data reduction technique used to generate mutually exclusive risk categories may also be a limitation.

This research has the potential to assist companies in standardizing the disclosure of risks within IPO prospectuses. The insights gained can inform market regulators in designing policies aimed at aiding investors in formulating investment strategies, ultimately enhancing transparency and clarity regarding information disclosure. Moreover, the findings offer valuable guidance to investors in selecting IPOs aligned with their risk tolerance levels.

From a societal perspective, this study represents advancements by guiding regulators towards developing and regulating standardized, mutually exclusive risk factors. Such measures can aid investors in enhancing their decision-making perspectives regarding IPOs, promoting a more informed and confident investment environment.

This study is a pioneering attempt to address knowledge gaps by identifying distinct categories of risk disclosures in IPO prospectuses and examining their potential influence on IPO underpricing in the financial and non-financial sectors in India.

This study examines how far female tour guides in Egypt experience sexual harassment and how they cope with it.

A qualitative research method is employed, and semi-structured interviews were conducted with 32 full-time female tour guides working for several travel agencies in Egypt. Thematic analysis was used to extract the main ideas from the transcripts.

The findings show that female tour guides in Egypt would encounter annoying gender harassment mostly from tourists they serve, and they might suffer from irresponsible behavior – gender harassment, unwanted sexual harassment, and sexual coercion – from their local managers. When facing sexual harassment, female tour guides usually tend to adopt one of the following three coping strategies: (a) indifference to sexual harassment they encounter, (b) heroism by taking legal action when exposed to sexual harassment or (c) fatalism by taking inconsequential action such as complaining the harasser to his direct manager or filling in an official complaint inside their workplace. The selection of the coping strategy is usually based on the female victim's personality and the organizational and social context she adapts to.

This paper contributes by filling a gap in tourism, human resources management and gender studies in which empirical studies on the sexual harassment that female tour guides encounter, particularly in non-Western contexts, have been limited so far.