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In 2018, the International Financial Reporting Standard 9-Financial Instruments became mandatory, effectively changing the underlying accounting principles of financial instruments. This paper systematically reviews the academic literature on the implementation effects of IFRS 9, providing a coherent picture of the state of the empirical literature on IFRS 9.

The study thrives on a systematic review approach by analyzing existing academic studies along the following three broad categories: adoption and implementation, impact on financial reporting, and risk management and provisioning. The study concludes by providing research prospects to fill the identified gaps.

We document data-related issues, forecasting uncertainties and the interaction of IFRS 9 with other regulatory standards as implementation challenges encountered. Also, we observe cross-country heterogeneity in reporting quality. Furthermore, contrary to pre-implementation expectations, we find improvement in risk management. This suggests that despite the complexities of the new regulatory standard on financial instruments, it appears to be more successful in achieving the intended objective of enhancing better market discipline and transparency rather than being a regulatory overreach.

As the literature on IFRS 9 is burgeoning, we provide state-of-the-art guidance and direction for researchers with a keen interest in the economic significance and implications of IFRS 9 adoption. The study identifies gaps in the literature that require further research, specifically, IFRS 9 adoption and firm’s hedging activities, IFRS 9 implications on non-financial firms. Lastly, existing studies are mostly focused on Europe and underscore the need for more research in under-researched jurisdictions, particularly in Asia and Africa. Also, to standard setters, policymakers and practitioners, we provide some insight to aid the formulation and application of standards.

Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from their facial expressions and neuro-signals. This study explores the potential for face recognition and neuro-marketing in modern-day marketing.

The study conducts an in-depth examination of the extant literature on neuro-marketing and facial recognition marketing. The articles for review are downloaded from the Scopus database, and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is then used to screen and choose the relevant papers. The systematic literature review method is applied to conduct the study.

An extensive review of the literature reveals that the domains of neuro-marketing and face recognition marketing remain understudied. The authors’ review of selected papers delivers five neuro-marketing and facial recognition marketing themes that are essential to modern marketing concepts.

Neuro-marketing and facial recognition marketing are artificial intelligence (AI)-enabled marketing techniques that assist in gaining cognitive insights into human behavior. The findings would be of use to managers in designing marketing strategies to enhance their marketing approach and boost conversion rates.

The uniqueness of this study lies in that it provides an updated review on neuro-marketing and face recognition marketing.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.

The years following the 9/11/2001 terrorists attacks saw a marked increase in community and hospital emergency preparedness, from communications across community networks, development of policies and procedures, to attainment and training in the use of biological warfare resources. Regular drills ensured emergency and health care personnel were trained and prepared to address the next large-scale crisis, especially from terrorist and bioterrorist attacks. This chapter looks at some of the more familiar global health issues over the past two decades and the lessons learned from hospital responses to inform hospital management in preparation for future incidents.

This study is a narrative review of the literature related to lessons learned from four major events in the time period from 2002 to 2023 – SARS, MERS, Ebola, and COVID-19.

The initial search yielded 25,913 articles; 57 articles were selected for inclusion in the study.

Comparison of key issues and lessons learned among the four major events described in this article – SARS, MERS, Ebola, and COVID-19 – highlight that several lessons are “relearned” with each event. Other key issues, such as supply shortages, staffing availability, and hospital capacity to simultaneously provide care to noninfectious patients came to the forefront during the COVID-19 pandemic. A primary, ongoing concern for hospitals is how to maintain their preparedness given competing priorities, resources, and staff time. This concern remains post-COVID-19.

This paper offers a definition of the core of information science, which encompasses most research in the field. The definition provides a unique identity for information science and positions it in the disciplinary universe.

After motivating the objective, a definition of the core and an explanation of its key aspects are provided. The definition is related to other definitions of information science before controversial discourse aspects are briefly addressed: discipline vs. field, science vs. humanities, library vs. information science and application vs. theory. Interdisciplinarity as an often-assumed foundation of information science is challenged.

Information science is concerned with how information is manifested across space and time. Information is manifested to facilitate and support the representation, access, documentation and preservation of ideas, activities, or practices, and to enable different types of interactions. Research and professional practice encompass the infrastructures – institutions and technology –and phenomena and practices around manifested information across space and time as its core contribution to the scholarly landscape. Information science collaborates with other disciplines to work on complex information problems that need multi- and interdisciplinary approaches to address them.

The paper argues that new information problems may change the core of the field, but throughout its existence, the discipline has remained quite stable in its central focus, yet proved to be highly adaptive to the tremendous changes in the forms, practices, institutions and technologies around and for manifested information.

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

This paper aims to conduct a systematic literature review on the fintech services and financial inclusion of the developing nations that particularly focuses on lower middle-income group nations (LMIGN) and upper middle-income group nations (UMIGN) to highlight the research areas that have not received attention and present opportunities for future research.

This paper adopts a systematic approach to examine 65 research articles published from 2016 to 2021, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

The study identifies research gaps in two key themes: backward and outward linkages. In backward linkages, the literature on UMIGN should pay attention to the behavioural patterns associated with lending, investment and market provision-related fintech services. Further research is needed to understand the relationship between fintech services on the usage and quality dimension of financial inclusion in both LMIGN and UMIGN. For outward linkages, future research work should explore the role of fintech and financial inclusion in the development of LMIGN. This study provides valuable insights and guides future research directions by comprehensively mapping the existing studies.

This study does not use quantitative tools, such as meta and bibliometric analysis, to validate the findings.

This research paper offers new perspectives that introduce a novel framework for analysing literature on fintech, financial inclusion and its impact on the overall development of UMIGN and LMIGN.

This study aims to investigate the prevalence and characteristics of dysphagia among adults with intellectual disabilities (ID) referred to speech and language therapy for swallow assessment, providing information on the demographic characteristics, referral trends, co-occurring health conditions and reasons for referrals highlighting the complex health-care needs of this population.

This study used a standardised patient data extraction method over a six-month period involving 74 adults with ID referred to speech and language therapy for swallow assessment.

This study revealed a high prevalence of dysphagia among adults with ID referred to speech and language therapy for swallow assessment. Increasing age and severity of ID were associated with an increased likelihood of swallowing difficulties. Co-occurring health conditions such as mobility difficulties, epilepsy and gastrointestinal conditions were prevalent, suggesting that adults with ID and swallowing difficulties are often living with complex health conditions. Choking incidents and hospital admissions were primary reasons for referral.

This study stresses the pressing need for strategies to mitigate risks associated with choking incidents and hospital admission among this vulnerable population. Possible limitations include a reliance on referral and the focus being on a single service over a short period which may limit generalisation to the wider ID population.

This study emphasises the need to understand each person’s unique profile of health needs and the value of a specialised speech and language therapy service.

The importance of increasing awareness among caregivers and medical experts is highlighted.

The findings underscore the importance of tailored assessment, caregiver involvement and heightened interdisciplinary awareness to effectively manage dysphagia in individuals with ID.

The article extends the distinction of semantic from syntactic labour to comprehend all forms of mental labour. It answers a critique from de Fremery and Buckland, which required envisaging mental labour as a differentiated spectrum.

The paper adopts a discursive approach. It first reviews the significance and extensive diffusion of the distinction of semantic from syntactic labour. Second, it integrates semantic and syntactic labour along a vertical dimension within mental labour, indicating analogies in principle with, and differences in application from, the inherited distinction of intellectual from clerical labour. Third, it develops semantic labour to the very highest level, on a consistent principle of differentiation from syntactic labour. Finally, it reintegrates the understanding developed of semantic labour with syntactic labour, confirming that they can fully and informatively occupy mental labour.

The article further validates the distinction of semantic from syntactic labour. It enables to address Norbert Wiener's classic challenge of appropriately distributing activity between human and computer.

The article transforms work in progress into knowledge for diffusion.

It has practical implications for determining what tasks to delegate to computational technology.

The paper has social implications for the understanding of appropriate human and machine computational tasks and our own distinctive humanness.

The paper is highly original. Although based on preceding research, from the late 20th century, it is the first separately published full account of semantic and syntactic labour.