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How do dark personality traits impact workplace bullying (WB)? How can organizations mitigate it? This study aims to explore the relationship between the Dark Tetrad (DT) (narcissism, psychopathy [PY], Machiavellianism and sadism [SM]) and WB, proposing the mediation role of moral disengagement (MD) and the moderation role of emotional stability (ES).

The proposed mediated-moderation model was tested on 404 employees working in the hospitality sector in Pakistan. Data have been analyzed through regression analysis and PROCESS macros to test the study’s hypotheses.

PY, Machiavellianism and SM positively relate to WB and MD mediates this relationship. A high level of ES reduces the intensity of the mediated influence of PY, Machiavellianism and SM on WB.

Developing targeted policies and practices (e.g. personality tests to build a good psychological architecture of the firm) and reviewing processes that support the moral justification of antisocial conduct can be beneficial for limiting WB. In addition, meditation, mindfulness training and supporting trust and cooperation within organizations can increase the mitigating effect of ES and, in turn, reduce WB.

To the best of the authors’ knowledge, this is the first study that explicitly verifies a mediated-moderation model on DT-WB while proposing a further explanation (i.e. mediation of MD) and a novel solution (i.e., moderation by ES).

Digitalization can drive the sustainable development of society and industry. However, a lot of the opportunities are tightly related to the risks. This chapter investigates how academic performance in terms of scientific publications at the intersection of digitization, sustainable development, and industry brings added value to the research field. The analysis focuses on the academic papers that research the relationship between digitalization, sustainable development, and industry, analyzing comparatively two primary academic databases, Scopus and Web of Science (WoS). To do that, two samples of scientific publications were investigated. The first sample comprised 703 documents indexed in WoS, while the second was formed by 566 documents indexed in Scopus. The analyzed period for WoS is 1996–2022, while for Scopus is 2000–2022. The empirical results indicated that the research field's primary issues include digitalization, digital transformation, the construction industry, sustainability, COVID-19, and innovation for both WoS and Scopus. The most relevant authors on this cross-cutting subject in the WoS database are Beier G., Aigbavboa C., and Kumar M. In contrast, in the Scopus database, we found Beier G., Ghobakhloo M., and Iranmanesh M. At the same time, the most typical journals are the International Journal of Production Research, Sustainability, and Computers in Industry for WoS and Sustainability, Journal of Cleaner Production, and Procedia CIRP for Scopus.

Purpose: As a result of the transition from the paradigm of ‘knowledge and skills’ learning to the university of uncertainty, the concept of VUCA has grown for the revision of various adaptive models of educational practices.

Need for Study: The primary goal is to explore the research field of the educational system and learning environments; the investigation of scientific knowledge is enabled by bibliometric analysis, revealing through it the fluctuations of the literature.

Methodology: To better view the historical evolution of publications in the educational system field, two data samples were integrated into this study, with the focus of the chapter being on the authors, keywords, articles, journals, subject analysis, word cloud analysis, and cluster analysis. The first includes 1,620 Web of Science-recorded documents published between 1991 and 2022, and the second sample comprises 159 Scopus-recorded papers published between 1978 and 2022.

Findings: The first empirical results show that interest in this subject escalated around 2008. The main concerns around this research field are the labour market, teaching-learning, technology, economic development, the medical field, and sustainability. After 2020, a new subject took amplitude, seemingly connected to the educational system and learning environment, that subject being ’COVID-19.

Practical Implications: The relationship between authors, keywords, and sources is illustrated through Sankey diagrams, from which valuable information can be extracted: nine of the Scopus authors have published articles in the ‘Journal of Higher Education Policy and Management’ documents that present the following list of keywords: ‘higher education’, ‘education’, ‘management’, ‘leadership’, and ‘tertiary education’.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.

This study aims to investigate multilingual representation on public signs in the High City tourist destination in Abha, Saudi Arabia. It also reveals the linguistic strategies used in such representation.

This exploratory qualitative study used purposive sampling to analyze bottom-up public signs collected from the target tourist destination. A preliminary analysis was conducted for a more in-depth qualitative analysis of every sign. An Excel database was used to provide a general description and a preliminary reading of the strategies before using an in-depth analysis of every sign.

The study revealed that monolingualism (Arabic or English) and bilingualism (Arabic and English) represented the High City as a tourist destination where the signs served information and symbolic functions. No single multilingual sign was found. Certain linguistic strategies were used on the public signs, including politeness, transliteration, hybrid representation, personification and fragmentary. Some tourist-oriented strategies, such as the crisis communication strategy, are still missing.

These findings indicate that this tourist destination still targets local and regional visitors, and its linguistic landscape (LL) needs further consideration in terms of internationalization and targeting international visitors. This study implies that bilingual Arabic and English tourist destinations are potential domains for translation students and English language learners.

This study has focused on the LL of a newly established tourist destination in Saudi Arabia. It has shed light on the nuanced representations and strategies used through public signage. It contributes to understanding how linguistic elements can shape tourists’ perceptions and experiences.

Quadrotors have been applied in various fields. However, because the quadrotor is subject to multiple disturbances, consisting of external disturbances, actuator faults and parameter uncertainties, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance. This paper aims to design a safety controller that uses observer and neural network method to improve the tracking performance of UAV under multiple disturbances. The experiments prove that this method is effective.

First, to actively estimate and compensate the synthetic uncertainties of the system, a finite-time extended state observer is investigated, and the disturbances are transformed into the extended state of the system for estimation. Second, an adaptive neural network controller that does not accurately require the dynamic model knowledge is designed based on the estimated value, where the weights of the neural network can be dynamically adjusted by the adaptive law. Furthermore, the finite-time bounded convergence of the proposed observer and the stability of the system are proved through homogeneous theory and Lyapunov method.

The figure-“8” climbing flight simulation and real flight experiments illustrate that the proposed safety control strategy has good tracking performance.

This paper proposes the safety control structure of the UAV, which combines the extended state observer with the neural network method. Numerical simulation results and actual flight experiments demonstrate the effectiveness of the proposed control strategy.

In the fourth industrial revolution, where business accounting integrates with automation through artificial intelligence (AI) and information communication technology (ICT), auditors must be able to access and analyze vast data and information to identify potential risks and issues. Using data analytics and AI to study significant amounts of data linked to audits, this study aims to investigate auditing practices by leveraging ICT and AI to enhance the audit process.

Bibliometric and quantitative research techniques have been used in the study’s mixed-method process. The theoretical underpinnings of AI have been investigated using the bibliometric research method, and the challenge of implementing ICT-enabled auditing practices among auditing professionals has been studied using the quantitative research method. Surveys, interviews and bibliometric analysis have all been used as data-gathering techniques.

Research in AI and auditing has a broad worldwide scope, involving developed and developing nations. ICT perceived benefits have no direct effect on auditing practices. However, ICT training has a mediating effect on the relationship between ICT perceived benefits and auditing practices. ICT adoption has no moderating effect on the relationship between ICT training and auditing practices.

Findings have significance for lead auditors, policymakers and the Institute of Chartered Accountants of India (ICAI), who are keenly interested in upgrading the auditing practice of accounting professionals in India by incorporating AI and ICT determinants.

This research makes a significant contribution by offering a thorough framework for improving the knowledge management of practising auditors regarding ICT adoption, training and perceived benefits, a crucial component of auditing practices in the digital age. In addition, it provides insightful information about how AI affects accounting practices, which may point the way for further study in this area.

This research has significant implications for auditing firms in India. It can inform ICAI, policymakers and regulators in their attempts to foster the incorporation of AI and ICTs in auditing practice.

In this paper the aim is that Aramid/alginate blended nonwoven fabrics were prepared, and the flame retardancy of the blended nonwoven fabrics was studied by thermogravimetric analysis, vertical flame test, limiting oxygen index (LOI) and cone calorimeter test.

The advantages of different fibers can be combined by blending, and the defects may be remedied. The study investigates whether incorporating alginate fibers into aramid fibers can enhance the flame retardancy and reduce the smoke production of prepared aramid/alginate blended nonwoven fabrics.

Thermogravimetric analysis indicated that alginate fibers could effectively inhibit the combustion performance of aramid fibers at a higher temperature zone, leaving more residual chars for heat isolation. And vertical flame test, LOI and cone calorimeter test testified that the incorporation of alginate fibers improved the flame retardancy and fire behaviors. When the ratio of alginate fibers for aramid/alginate blended nonwoven fabrics reached 80%, the incorporation of alginate fibers could notably decreased peak-heat release rate (54%), total heat release (THR) (29%), peak-smoke production rate (93%) and total smoke production (86%). What is more, the lower smoke production rate and lower THR of the blends vastly reduced the risk of secondary injury in fires.

This study proposes to inhibit the flue gas release of aramid fiber and enhance the flame retardant by mixing with alginate fiber, and proposes that alginate fiber can be used as a biological smoke inhibitor, as well as a flame retardant for aramid fiber.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.